After the said financial crisis, several governing bodies especially in the United States started evolving a regulatory environment of sorts. Many new reforms were introduced most notably the Dodd–Frank Wall Street Reform and Consumer Protection Act[1] by the United States Congress and Basel I, II and III namely by the Basel Committee on Banking Supervision (BCBS). It proposed to make changes in the American financial regulatory environment that affected all federal financial regulatory agencies and almost every part of the nation’s financial services industry. Nowadays, banks and financial institutions are required to develop dynamic systems so as to keep themselves regulated according to these reforms. This can be owed to the increased requirement of information reporting, audits, calculations etc. A solution that automates and streamlines the process to generate reports in a timely fashion is the need of the hour.

But this is easier said than done. Regulatory requirements differ across the globe. Some governments follow an approach where the whole banking spectrum is under their control, thus limiting the entry of new players in the market. On the other hand, some governments do not directly control banks but put such restrictions in place which compel them to disclose accurate information at a timely interval in order to keep a check on them. Whatever the case, regulatory reporting is a vital cog in the banking business and is here to stay. So what makes it all so important? Basically, regulators want all the smoke screens removed between them and their entities when it comes to matters like liquidity management, asset liability management, foreign exchange exposure, risk management and the entity’s financial health. Once data is collected this way, there’s a much lower chance of the reporting bank going bankrupt as there still is time to ensure that corrective measures are in place and the bank’s profitability is not threatened. It can be compared to a type of early warning system specially crafted for banking organizations.

After the “Great Recession”, regulators across the world have tightened their clout over financial markets and various new regulations have been introduced to ensure the following[2]:

• Avoid large bailouts as they put immense political, social and economic pressure on countries.
• Increase consumer protection by spreading awareness in the upside and the downside of their investment decisions.
• Improve transparency and accountability of financial instruments like derivatives, options and embedded derivatives, associated cash flows from the finance and risk perspective.

So why do financial institutions find it tough to meet regulatory reporting?

Nowadays, banks need to collate data for various reports like Financial Reporting (FINREP), Foreign Account Tax Compliance Act (FATCA), Common Reporting Standards (CRS) and BASEL. With so much of load on the current legacy banking systems, they are working to their limit and cannot handle all the extra information generated with ease. Some of the key challenges faced by banks in such a scenario are:

1. Multiple sources of information: Banks do not have a single source of information. Depending on the depth of information required, banks need to collate data from different sources to comprehensively meet reporting requirements. At times, this requires development of IT applications which have an impact on the time and cost aspects of the reporting exercise.
2. Multiple report formats: Depending on the nature of business, banking systems need to file reports in different formats as specified by the governing regulator. Organizational systems and processes in most cases are not flexible enough to create new, or modify existing, data models to meet changing requirements.
3. Lack of skilled resources: Changing reporting requirements mean that banks not only need finance experts, but also require professionals skilled in mapping business requirements to existing IT systems, developing new applications if and where needed, and so on.
4. Inaccuracy of data: Banks exchange information through financial statements, risk reports, submissions for capital adequacy, and regulatory reports. All these reports are generated from different systems, and banks need effective reconciliation processes to cross-check the accuracy of data across systems.
5. Stringency of timelines: Regulators expect financial institutions to quickly alter their internal processes to meet the modified reporting requirements within scheduled time frames. Though prior information on these implementation dates is shared with banks, more often than not, the time is not sufficient to action the required changes.

Eventually, the question arises as how to cope up with evolving regulations?

In this regard, it is very important to understand the role that technology plays in the ever changing regulatory setup to meet the reporting requirements. Today, in the market there are many tools and software present but the demand for newer technology increases with every new regulation introduced. These regulatory reporting tools help banks avoid inherent problems like legacy system issues, lack of granular data, excessive system feed and complexity in data mapping, compatibility for data feeds.

Hexanika: Regulatory Reporting Made Easy

Hexanika is a FinTech Big Data software company, which has developed an end to end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance.

Hexanika helps establish a compliance platform that streamlines the process of data integration, analytics and reporting. Our software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

To know more about our products and solutions, read: http://hexanika.com/company-profile/

Contributor: Akash Marathe

Feature Image Link: https://www.pexels.com/photo/working-business-money-coins-34204/

[1] Source: https://en.wikipedia.org/wiki/Dodd%E2%80%93Frank_Wall_Street_Reform_and_Consumer_Protection_Act

[2] Source: http://www.tcs.com/resources/white_papers/Pages/Regulatory-Reporting-What-Banks-can-do.aspx

About the author

Until 2014, Prakash Jalihal served as Managing Director and senior vice president of Fidelity Information Services (FIS) for Latin America and the Caribbean and was responsible for the direct management of sales and operations for the company’s business in that region The company now operates in over 30 countries in the region and is rated #1 Financial Technology company by FinTech.

Jalihal began his career with FIS, formerly Alltel Information Services and formerly Systematics in 1972. Prior to his current position, he has held various positions: Programmer/analyst, Systems Development Division manager, Planning and Research manager, Little Rock Processing Center manager, Global Client Operations manager.

Before joining Fidelity Information Services, Jalihal was a production control engineer with International Harvester Company in Bombay, India.

He earned a Bachelor of Science in Mechanical Engineering from the University of Bombay in 1970 and a Master of Science in Industrial Engineering from the University of Arkansas in 1972.

From 1995 through 2001, Mr. Jalihal served on the Global Advisory Board for the Thunderbird School for International Management and is currently on Dean’s Advisory Council for the University of Arkansas Engineering School.

Mr. Jalihal currently serves on the Board of EAST Initiative, a non-profit organization established to promote Environmental and Spatial Technology in schools in Arkansas and surrounding states, based on project based learning. He also serves on the Advisory Board of the non-profit Best Buddies Arkansas.

In 2009, he was inducted into the Arkansas Academy of Computing and in 2011, he was inducted into Arkansas Academy for Industrial Engineers.

In 2014, Mr. Jalihal joined the Global Technology Deployment Initiative as a Senior Advisor. The charter of GTDI is to provide funding and deploy technology in the countries that lack funding and do not have a technology distribution channel.

In 2014, he was also elected as a member of Arkansas Accelerator, an economic development advocacy group that promotes creation of high paying jobs and formation of new companies as part of a knowledge based economy.

Originally published at hexanika.com on August 22, 2016.

Traditionally, data integration has meant compromise. No matter how rapidly data architects and developers could complete a project before its deadline, speed would always come at the expense of quality. On the other hand, if they focused on delivering a quality project, it would generally drag on for months thus exceeding its deadline. Finally, if the teams concentrated on both quality and rapid delivery, the costs would invariably exceed the budget. Regardless of which path you chose, the end result would be less than desirable. This led some experts to revisit the scope of data integration. This write up shall focus on the same issue.

Why is data integration so difficult?

Even after years of creating data warehouses and data infrastructures, IT teams have continued to struggle with high costs, delays and sub optimal results of traditional data integration. With the introduction of new data sources and data types, data management professionals are not making any concrete progress. Rising business demands are another factor. Following are some of the main causes for data inefficiency:

1. Data integration is a time consuming process owing to lack of reusability of various data patterns.
2. Integration costs are tremendous.
3. Low quality of data results in untrustworthy data.
4. Scalability is the biggest issue in traditional data systems as the data volumes keep increasing each day.
5. Lack of real-time technologies results in data not updated regularly.

These problems and challenges are all related to the reality that data has become more fragmented while data integration has grown more complex, costly and inflexible. Depending on the data integration requirements, the data must first be extracted from the various sources, then it is generally filtered, aggregated, summarized or transformed in some way and finally delivered to the destination user or system.

Dramatic changes in data volume, variety and velocity make the traditional approach to data integration inadequate and requires one to evolve to next-generation techniques in order to unlock the potential of data.

For any data integration project one has to have a good understanding of the following:

• Data models within each data source
• Mappings between the source and destination data models
• Data contracts mandated by the destination system
• Data transformations required

Next Generation Data Integration

Next-generation data integration technologies are designed mostly to support an extended team that includes data, integration and enterprise architects, as well as data analysts and data stewards, while better aligning with business users.

Most companies use a mixture of in-house developed systems, 3rd party “off the shelf” and cloud hosted systems. In order to get the most value from the data within these systems it must be consolidated in some way. For example, the data within a cloud hosted system could be combined with the data in an in-house system to deliver a new product or service. Mergers and acquisitions are also big drivers of data integration. Data from multiple systems must be consolidated so that the various companies involved in the merger can work together very effectively.

Some rules for next generation data integration are as follows[2]:

1. Data Integration (“DI”) is a family of techniques. Some data management professionals still think of DI as merely ETL tools for data warehousing or data replication utilities for database administration. Those use cases are still prominent, as we’ll see when we discuss TDWI survey data. Yet, DI practices and tools have broadened into a dozen or more techniques and use cases.
2. DI techniques may be hand coded, based on a vendor’s tool, or both. TDWI survey data shows that migrating from hand coding to using a vendor DI tool is one of the strongest trends as organizations move into the next generation. A common best practice is to use a DI tool for most solutions, but augment it with hand coding for functions missing from the tool.
3. DI practices reach across both analytics and operations. DI is not just for Data Warehousing (“DW”). Nor is it just for operational Database Administration (“DBA”). It now has many use cases spanning across many analytic and operational contexts and expanding beyond DW and DBA work is one of the most prominent generational changes for DI.
4. DI is an autonomous discipline. Nowadays, there’s so much DI work to be done that DI teams with 13 or more specialists are the norm; some teams have more than 100. The diversity of DI work has broadened, too. Due to this growth, a prominent generational decision is whether to staff and fund DI as is, or to set up an independent team or competency center for DI.
5. DI is absorbing other data management disciplines. The obvious example is DI and Data Quality (“DQ”), which many users staff with one team and implement on one unified vendor platform. A generational decision is whether the same team and platform should also support master data management, replication, data sync, event processing and data federation.
6. DI has become broadly collaborative. The larger number of DI specialists requires local collaboration among DI team members, as well as global collaboration with other data management disciplines, including those mentioned in the previous rule, plus teams for message/service buses, database administration and operational applications.
7. DI needs diverse development methodologies. A number of pressures are driving generational changes in DI development strategies, including increased team size, operational versus analytic DI projects, greater interoperability with other data management technologies and the need to produce solutions in a more lean and agile manner.
8. DI requires a wide range of interfaces. That’s because DI can access a wide range of source and target IT systems in a variety of information delivery speeds and frequencies. This includes traditional interfaces (native database connectors, ODBC, JDBC, FTP, APIs, bulk loaders) and newer ones (Web services, SOA and data services). The new ones are critical to next generation requirements for real time and services. Furthermore, as many organizations extend their DI infrastructure, DI interfaces need to access data on-premises, in public and private clouds and at partner and customer sites.
9. DI must scale. Architectures designed by users and servers built by vendors need to scale up and scale out to both burgeoning data volumes and increasingly complex processing, while still providing high performance at scale. With volume and complexity exploding, scalability is a critical success factor for future generations. Make it a top priority in your plans.
10. DI requires architecture. It’s true that some DI tools impose an architecture (usually hub and spoke), but DI developers still need to take control and design the details. DI architecture is important because it strongly enables or inhibits other next generation requirements for scalability, real time, high availability, server interoperability and data services.

Some of the methods employed for next generation of data integration are as follows[3]:

1. Extract, Transform & Load (“ETL”) Design: ETL involves extraction of data from a source and transforming it in the desired data model and finally loading the data into the destination data store. In short:

• Extraction process involves extracting the source data into a staging data store which is usually a separate schema within the destination database or a separate staging database.
• Transform process generally involves re-modelling the data, normalizing is structure, unit conversion, formatting and the resolution of primary keys.
• Load process is quite simple in the case of a once off data migration but can be quite complex in the case of continuous data integration where the data must be merged (inserts, updates & deletes), possibly over a defined date range, there may also be a requirement to maintain a history of changes.

1. Enterprise Application Integration Design: While the ETL process is about consolidating data from many sources into one, Enterprise Application Integration, or EAI, is about distributing data between two or more systems. Data exchanged using EAI is often transactional and related to an event in a business process or the distribution of master data. EAI includes message broker and Enterprise Service Bus (“ESB”)
2. Data Virtualization & Data Federation: Data virtualization is the process of offering data consumers a data access interface which hides the technical aspects of stored data, such as location, storage structure, access language and storage technology. Data federation is a form of data virtualization where the data stored in a heterogeneous set of autonomous data stores is made accessible to data consumers as one integrated data store by using on-demand data integration.

Hexanika: Implementation of Data Integration in the form of ETL Process

Hexanika is a FinTech Big Data software company, which has developed a revolutionary software named as Hexanika Solutions for financial institutions to address data sourcing and reporting challenges for regulatory compliance. What it includes is basically uploading the data, sanitizing it and then validating the data. Hexanika Solutions can join ’N’ different tables data sources and creates joins. These data checks are applied on standardized data as per customer needs. Scalability isn’t an issue as the platform used is Hadoop.

The ELT Process[4]

Hexanika leverages the power of ELT using distributed parallel processing, Big Data/Hadoop technology with a secure data cloud (IBM Cloud). Understanding the high implementation costs of new systems and the complexities involved in redesigning existing solutions, Hexanika offers a unique build that adapts to existing architectures. This makes our solution cost-effective, efficient, simple and smart!

Read more about our solution and architecture at: http://hexanika.com/big-data-solution-architecture/

Also, do visit our website: www.hexanika.com

Contributor: Akash Marathe

Feature image link: https://i.ytimg.com/vi/7TpG9w46i_A/maxresdefault.jpg

[1] Source Link: https://en.wikipedia.org/wiki/Data_integration

[2]Source Link: ftp://public.dhe.ibm.com/software/data/…/TDWI_NextGenerationDataIntegration.pdf

[3] Source Link: http://iasaglobal.org/itabok/capability-descriptions/data-integration/

[4] Image Credits: IBM

About the author

Until 2014, Prakash Jalihal served as Managing Director and senior vice president of Fidelity Information Services (FIS) for Latin America and the Caribbean and was responsible for the direct management of sales and operations for the company’s business in that region The company now operates in over 30 countries in the region and is rated #1 Financial Technology company by FinTech.

Jalihal began his career with FIS, formerly Alltel Information Services and formerly Systematics in 1972. Prior to his current position, he has held various positions: Programmer/analyst, Systems Development Division manager, Planning and Research manager, Little Rock Processing Center manager, Global Client Operations manager.

Before joining Fidelity Information Services, Jalihal was a production control engineer with International Harvester Company in Bombay, India.

He earned a Bachelor of Science in Mechanical Engineering from the University of Bombay in 1970 and a Master of Science in Industrial Engineering from the University of Arkansas in 1972.

From 1995 through 2001, Mr. Jalihal served on the Global Advisory Board for the Thunderbird School for International Management and is currently on Dean’s Advisory Council for the University of Arkansas Engineering School.

Mr. Jalihal currently serves on the Board of EAST Initiative, a non-profit organization established to promote Environmental and Spatial Technology in schools in Arkansas and surrounding states, based on project based learning. He also serves on the Advisory Board of the non-profit Best Buddies Arkansas.

In 2009, he was inducted into the Arkansas Academy of Computing and in 2011, he was inducted into Arkansas Academy for Industrial Engineers.

In 2014, Mr. Jalihal joined the Global Technology Deployment Initiative as a Senior Advisor. The charter of GTDI is to provide funding and deploy technology in the countries that lack funding and do not have a technology distribution channel.

In 2014, he was also elected as a member of Arkansas Accelerator, an economic development advocacy group that promotes creation of high paying jobs and formation of new companies as part of a knowledge based economy.

Originally published at hexanika.com on July 25, 2016.

To cite a recent example, a friend who was interested to buy a car filled in a form (on paper in one of the company’s showroom) for a test drive. The friend mentions that she did not google any car information or lookup related websites; neither did she post anything relevant about it anywhere. However, just minutes after she had a call from the car company to confirm the time and date for the test drive, she got a call from her bank asking if she was interested in a car loan. The timing and the speed with which her information was out took her by surprise.

Enterprises today mine customer data to ensure maximum success by targeting their products and solutions to the right audience. Let us have a look at how Big Data and Customer Analytics are helping businesses use their customer data for maximum benefits.

How does Big Data fit into customer analytics?

Big Data is a cluster of extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions[1]. What the definition clearly mentions is that Big Data is not just related to storage and security of large data, it also includes the managing, handling, and analyzing these large data sets to gain valuable insights.

Today, organizations have tons of customer data which needs to be properly mined to help give their business an edge over its competitors. However, without the proper tools to accurately make sense of this data, organizations are lost and can make the wrong choices. This is where Big Data comes into the picture. To help businesses make key decisions based on the data from customer behavior, customer analytics is being implemented in almost all departments.

Big Data can not only handle large amounts of data, but with the power of Big Data Analytics, enterprises gain the ability to convert raw data into visual representations that can help to make it easier to trace patterns, understand trends and make associations in a concise and precise manner. Thus, Big Data gives businesses an edge in storage, analytics, mining and security of data.

Why do banks analyze customer data?

According to IBM’s Global Industry Leader in Banking and Financial Markets Likhit Wagle, “Although 80% of CEOs believe they offer customers superior services, only 8% of their customers agree.”[2] This statement just goes on to showcase how much more banks and financial institutions need to be doing to keep their customers happy.

Banks are dealing with huge amounts of data that surge in through disparate sources and which are stored in different formats. Analysis of this data is important in AML detection, but the same process can also help banks understand their customer’s needs to help give better services. Using Big Data to analyze a customer’s spending habits, banks can give fraud alerts to its customer using phone calls from credit card issuers about an unusual purchase. Banks can also use the same data to target particular privileges and added features that a card provides to the customer after understanding how and where the customer spends.

Banks can use customer data for[3]:

• Target Marketing: Identify potential clients for their products and services using data from various channels.
• Customer Services: Improve services to current customers by segregating them based on geographical locations, technology, transaction analysis, etc.
• Decision Making: Customer data is taken into consideration to help banks create suitable services, loan options, products, insurance options, etc. to cater to the needs and requirements of its customers.

The nation’s four large universal banks (JP Morgan Chase, Bank of America, Citigroup, and Wells Fargo & Co.) are using Big Data to harness the power of customer analytics and are using it in various other domains to extract insights that can help give them a competitive edge[4]. Even Cleveland-based KeyBank has moved to making data driven decisions using Big Data[5]. Banks and financial institutions are understanding the advantages of using Big Data technology and have started or have in place plans to implement Big Data and Analytics based solutions to get to know their customers better. According to IDC, the banking sector in the US spent $1.8 billion in 2014 on Big Data[6].

Why Hexanika?

HEXANIKA is a FinTech big data software company which has developed an end-to-end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Hexanika’s solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

To know about Hexanika’s Big Data Analytics benefits, see: http://hexanika.com/big-data-analytics/

Contributor: Vedvrat Shikarpur

Image: geralt

[1] Google Search: What is Big Data?

[2] Forbes: How Big Data Helps Banks Personalize Customer Service

[3] Nerdwallet: Banks Mine Big Data to Get to Know You Better, and Better

[4] The Wall Street Journal: Banks Using Big Data to Discover ‘New Silk Roads’

[5] Forbes: KeyBank Moves To Data Driven Decision Making

[6] CIO: IDC says big data spending to hit $48.6 billion in 2019

About the Author:

T. K. Chopra served as a Director of Financial Compliance of Bunge Ltd. since June 1, 2007. He also served as its Principal Accounting Officer and Controller. Prior to this, he spent 25 years at Citigroup in London, New York and Sao Paulo in a variety of financial management roles.

Originally published at hexanika.com on July 18, 2016.

So what is a sandbox and why is it taking the FinTech and regulatory world by storm? Let’s find out:

What is a Sandbox?

A sandbox is an isolated environment that allows a program to be executed without affecting the application it is running on[2]. It is used by developers to test new code. It acts as a virtual machine in a controlled environment, hence making sure that the program does not harm or alter any data in the server or the host system. This allows developers to test their programs before implementation in the main system in a secure and safe environment.

How will it benefit FinTech startups?

Microsoft and Markit have written to the Treasury Department’s Office of Comptroller (OCC) regarding a need for the US regulators to bring in an environment similar to the one implemented by the FCA. Markit asked the OCC whether companies “have appropriate space to breathe, to develop and test innovative solutions without fear of enforcement action and regulatory fines,” in a letter submitted May 31. Microsoft put forth a demand for a sandbox, stressing on a need for a US version of FCA’s FinTech approach. Law firm Morrison Foerster and Citigroup echoed this demand.[3]

Some of the benefits of a sandbox for the FinTech and financial services industry are as follows:

• Financially viable: In order to encourage innovation, various sandbox providers like FinTech Sandbox provide free data or subscriptions, which would otherwise add to the financial strain on startups operating within a limited capital. The environment is also freely made available to subscribers, saving the additional costs startups might have incurred to deploy virtual machines before actually implementing their products.
• Secure testing platform: As quoted by Christopher Woolard, FCA’s Director of Strategy and Competition, “The sandbox environment will allow startups to test their ideas without immediately incurring the normal regulatory consequences”. [4] This will allow startups to test their products and services for proof of concept, and the FCA is providing individual guidance, rule waivers and modifications and in some cases “no enforcement action letters”. [5]
• Access to APIs: Various sandbox providers also give access to banking data which helps organizations test, analyze and visualizes data to help get a real world use case. FinTech sandbox in particular is giving access to SEC-filed data which includes filings and disclosures. Access to such APIs is normally restricted by banks and financial institutions and also the regulatory bodies. [6] The FCA too states that it will offer public data sets provided by private firms to organizations registered to use the platform.[7]

About Hexanika:

HEXANIKA is a FinTech big data software company which has developed an end-to-end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Hexanika’s solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

Contributor: Vedvrat Shikarpur

Image Credits: cdu445

[1] Crowdfund Insider: FCA Prepares UK #FinTech “Regulatory Sandbox” for Early May

[2] Techtarget: Definition of Sandbox

[3] CNBC: Banks and start-ups debate regulation

[4] FCA: Innovate Finance Global Summit

[5] FCA: Regulatory Sandbox

[6] Finance Magnates: FinTech Sandbox Adds EDGAR Online for Firms to Access SEC Filings

[7] The Asian Banker: Innovation the “regulatory sandbox” way

About the Author

Huma has 18+ years experience working in banking and management consulting. She has an MBA from University of North Carolina and has worked for ORIX Group, Citigroup, Deloitte Consulting and Gartner. She is a seasoned leader with a track record of revenue growth as business head with P&L responsibility. Her areas of expertise include Business Development, Product management, Marketing and Sales Operations.

Originally published at hexanika.com on June 13, 2016.

According to the infographics created by DealSunny, a company that specializes in special offers and coupons, by the end of 2015, there are over 1362 FinTech companies spread across 54 countries in the world. Most of these are startups, and have collectively raised $25.8 billion in funding from investors in 2015. That brings the average investment to $44 million per company. Various FinTech experts also go on to say that United States and United Kingdom offer the best ecosystem, followed by Israel. These companies are clustered around Tel Aviv in Israel, London in UK, and Silicon Valley, Los Angeles & Boston in the US. The United States has the largest share in the pie, with $10 billion of FinTech investments.

If we look at the global investments in FinTech from 2010–15, the US alone raised $31.6 billion, Europe raised $4.4 billion with the overall figure going up to $49.7 billion. The investments have tripled from $4.05 billion in 2013 to $12.21 billion in 2014. The figure, as we can see, has doubled from 2014 to 2015.[1]

Going by the growing need for FinTech solutions for banks and financial institutions globally, it is safe to say that FinTech has come of age and the demand is only going to get higher in 2016. We have done a short analysis of the scope and rise of FinTech in the recent decades.

What is FinTech?

The term FinTech was coined to explain the diaspora of IT enterprises in the banking and financial sector. The 2008 financial crisis revealed that despite significant investments in sophisticated data management systems, banks were unable to cope with growing risks and regulatory changes. Sensing an opportunity, various tech companies started offering banks a new approach, methodology and tools to address issues related to data management, governance and compliance.

This led to a huge plethora of technology enterprises focused on dealing with issues faced by banks and financial institutions, out of which regulatory reporting and compliance was considered to be the most important.[2] The frequent reworking of financial regulations, constant need to manage and analyze data and tap customer behavior has led to FinTech innovations being high in demand for the last few years.

The rise of FinTech has opened up a world of possibilities. Businesses can offer more services for a fraction of the price of what it would have cost before.[3] Let’s take a look at how it is transforming banking.

How is FinTech Transforming Banking?

Consultant Oliver Wyman estimates that for a global banking industry generating $US5.7 trillion of revenue today, around $US1 trillion of revenue and costs could be reallocated by FinTech disruption[4]. And this is only the beginning!

FinTech is bringing innovative solutions to banking, lending and capital markets, and it is not just thousands of startups competing for the prize, big banks have joined in too. Large banks like JPMorgan Chase & Co, Citi, HSBC, and Barclays are investing heavily in FinTech research and supporting rising startups through accelerator programs to bring innovative new solutions to their customers.

Quoting JPMorgan CEO Jamie Dimon, “Fintech has been great at making it easier and often less expensive for customers and will likely lead to many more people, including more lower-income people, joining the banking system in the United States and abroad.” [5] James Eyers wrote for the Financial Review that, “FinTech has become ingrained in the strategic thinking of global banks because it is both a disruptive threat and an opportunity to enhance customer service and reduce costs.”

Large banks are investing heavily in FinTech (as the infographic shows) and small and medium banks too are doing their bit to ensure their customers get the best of technology. From mobile banking apps to giving customers more options through technology, banks are not only simplifying their processes but also doing more with customer data. With Big Data and Analytics, banks are using data to target their products and solutions to the right audience, and this is only making things easier and better for the customer.

“That’s the power of FinTech: it gives the power financial services use to provide only to the wealthy and makes them accessible to all”, writes Jason Raznick for MONEY, USA News.

How is Hexanika revolutionizing FinTech?

Hexanika is a FinTech Big Data software company which has developed an end-to-end regulatory compliance solution. The innovative machine learning big data solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

Hexanika helps establish a compliance platform that streamlines the process of data integration, analytics and reporting. The software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. The solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

Celebrating the win with Sandy Carter, General Manager of IBM Ecosystems and Social Business Evangelism, IBM

FinTech startups like Hexanika have also found recognition amongst entrepreneurs and investors. Hexanika was recently awarded by TiEcon with the ‘TiE50 2016 Top Start-up Winner’. Moreover, we were recognized in the list of ’40 RegTech startups to follow’ that is compiled byJan-Maarten Mulder, a Banking Executive and FinTech & Data Investor at ABN AMRO. The continued support from our partners IBM and Synpulse goes on to show that FinTech is here to make things better for banks and financial institutions.

Gary Norcross, President & CEO FIS (center) with Huma Usmani, CMO Hexanika (left) and Yogesh Pandit, CEO Hexanika (right)

And we have more good news! Hexanika has been selected for the VC FinTech Accelerator program which kicked off Monday, May 16, 2016 in Little Rock, Arkansas. The Venture Center (VC), in collaboration with Fidelity National Information Services (FIS Global) announced that it received 150 approved applications from all over the world out of which, ten companies have been selected for the accelerator program. For more information, please see: http://eepurl.com/b2ajK9

To know more about our products and solutions, read: http://hexanika.com/company-profile/

Author: Huma Usmani

Contributor: Vedvrat Shikarpur

Feature Image: markmags via Pixabay

[1] https://www.dealsunny.com/blog/infographic/fintech-digitally-disrupting-the-financial-world-infographic

[2] http://hexanika.com/regtech-is-the-new-fintech/

[3] https://www.hottopics.ht/stories/finance/what-is-fintech-and-why-it-matters/

[4] http://www.afr.com/business/banking-and-finance/fintech-a-us1-trillion-fight-20160508-gop1r2#ixzz49ZOBi1ZO

[5] http://money.usnews.com/money/blogs/the-smarter-mutual-fund-investor/articles/2016-05-10/the-fintech-revolution-is-just-beginning

Big Data is a technology-driven movement and its strategic importance requires special focus and attention during its adoption, owing to the following[2]:

The technology to support Big Data is evolving rapidly which takes time to mature.

1. While the three Vs (Volume, Velocity and Variety) have been regularly used to define Big Data, the use of technologies that support Big Data are not confined only to the way the three Vs are defined. Their usage has a much broader scope when correctly analyzed from the organization’s perspective.
2. The effective utilization of Big Data requires a change in mindsets regarding the way it is used. While other technologies help solve problems like streamlining the inventory management process or providing an online system enabling the order shipment tracking on a real-time basis, Big Data helps find the problems that require attention. For example, the combination of factors that causes defects in the manufacturing process, or the factors contributing to the sales differential between two stores.

Much like other evolving technologies, it is useful to first conduct small-scale projects in Big Data, for a better understanding of the technology and the business areas that may benefit from it. While these projects (more like proofs-of-concept) are good as a starting point, the mainstream adoption of Big Data requires a structured framework. This is because the solution space in this area comprises not only large, isolated and varied data sets, but also a rapidly evolving technology landscape. Trying to get all the requirements defined at one go and selecting the technology of choice at the outset can potentially derail the entire program and leave the organization with a dead investment.

Adoption Framework:

A key factor in the success of any new program is the way it is approached from the inception phase itself. Any Big Data program that requires the integration of data with strategic planning is going to be critical and will carry heavy penalties in case of failure. The right framework to enable the adoption of Big Data analytics within the organization must be adopted. The critical components of this framework include:

• Data discovery
• Analytics discovery
• Tools and technology discovery
• Infrastructure discovery
• Implementation

Big Data Adoption Process[3]

Recommendations for Big Data Adoption:

Driven by the need to solve business challenges, in light of both advancing technologies and the changing nature of data, banking and financial markets companies are starting to look closer at Big Data’s potential benefits. To extract more value from Big Data, we offer a broad set of recommendations tailored to banks and financial markets firms.

1. Commit initial efforts to customer-centric outcomes.
2. Define Big Data strategy with a business-centric blueprint.
3. Start with existing data to achieve near-term results.
4. Build Analytics capabilities based on business priorities.
5. Create a business case based on measurable outcomes.

Once adopted, Big Data technologies shall change the financial services industry in the following ways[4]:

1. Machine learning is set to accelerate and shall gain momentum when applied with fraud and risk sectors. This gain in momentum shall come from education and real world applications like financial markets, banking etc.
2. The gap between market leaders who utilize Big Data services and others which do not is set to increase as more and more businesses understand the importance of Big Data.

Data lineage, governance and data compliance shall be more deeply embedded with Big Data platforms to find a more comprehensive solution to handle compliance mandates, and to upscale their current legacy systems to newer and faster software services. Hadoop is at an advantage here since many new platforms can reach beyond current and legacy systems to give a holistic view of the regulatory scene today.

How Hexanika makes use of Big Data?

Hexanika is a FinTech Big Data software company which has developed an end-to-end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Hexanika’s innovative solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

Hexanika’s unique Big Data deployment approach by experienced professionals will simplify, optimize and reduce costs of deployment. It strives to achieve this by following the process as shown below:

Big Data Road-map[5]

Hexanika addresses Big Data using its unique product and solutions. To know more about us, see: http://hexanika.com/company-profile/

Feel free to get in touch with our experts to know more at: http://hexanika.com/contact-us-big-data-company/

Author: Tk Chopra

Contributor: Akash Marathe

Image Credits: www.publicdomainpictures.net

[1] Source: http://www.datawatch.com/2015/10/09/a-new-trend-emerges-in-big-data-adoption/

[2] Source: http://www.tcs.com/resources/white_papers/Pages/Big-Data-Adoption.aspx

[3] Source: http://www.ibm.com/services/multimedia/Analytics_The_real_world_use_of_big_data_in_Financial_services_Mai_2013.pdf

[4] Source: https://www.mapr.com/blog/top-10-big-data-trends-2016-financial-services

[5] Source: http://hexanika.com/history-of-big-data/

As regulatory expectations continue to rise, there is an increased need to emphasize each institution’s ability to react to regulatory scrutiny. With multiple complex regulatory deadlines for 2016 staring in the face of financial institutions, managing regulatory compliance and risk has never been so complex.

We take a look at what led to banks being subjected to so many regulations and how size plays a very important role in the regulatory burden faced by small and midsize banks:

2008: The Financial Crisis, New Regulations & Regulators

The financial crisis of 2008 significantly altered the banking landscape, and the number of new banking regulations increased with the passage of legislation like the Dodd-Frank Act & Basel III Reforms. The objective of these new regulations was to restrict ‘too-big-to-fail’ large banks from investing large sums in risky activities, in turn ensuring a situation like 2008 does not repeat again.

However, the addition of new regulatory bodies and regulations has added to the regulatory requirements that banks now need to meet. As our article on Regulatory Impact on Small and Midsize Banks suggests, the current regulatory environment has become particularly stressful to community and medium sized banks which are not as well resourceful as the large banks to comply with these additional requirements.

Hester Peirce, Ian Robinson, and Thomas Stratmann of George Mason University’s Mercatus Center surveyed community bankers and found that the median compliance staff for respondents doubled from 1 to 2 in the three years following enactment of Dodd-Frank.[1]

Numbers of Newly Created Banks (De Novos)

Image Source: Richmond Fed: Explaining the Decline in the Number of Banks since the Great Recession

How Large & Small Banks Are Regulated?

According to survey findings released during the 2015 Community Banking in the 21stCentury research and policy conference, community banks spent an estimated $4.5 billion in 2014 for complying with the various banking regulations. These expenses are part, but not necessarily all, of a bank’s overall compliance requirements.[2] These findings just go on to show how size is one of the factors that policymakers need to consider when applying regulations to a bank and deciding how a bank needs to be supervised.

Community banks spent an estimated $4.5 billion in 2014 for complying with the various banking regulations.

A whitepaper ‘The Impact of Dodd-Frank on Community Banks’ opined, “The major flaw of the federal banking regulatory system is that it treats a community bank with $165 million in assets (the median-sized American bank) as the same essential creature as JP Morgan Chase or Bank of America.”[3] The current banking regulation systems follow a “unitary model”, which is a system where the broad principles underlying bank regulations are same for all banks, regardless of size and activities. However, as Federal Reserve Governor Daniel Tarullo puts it, Dodd-Frank Act and other reforms require “different categories of banking organizations — largely, but not exclusively, on the basis of total assets — to which different regulatory requirements are to apply.”[4]

President and CEO of community bank holding company Northwest Financial Corp Jeff Plagge says, “All too often, regulation intended for the largest institutions becomes the standard that is applied to every bank. This approach layers on unnecessary requirements that do little to improve safety and soundness, but add significantly to the cost of providing services.”[5]

The need of the hour

Referring to the 2015 Community Banking survey above, surveyed banks indicated that regulatory compliance accounted for:

• 11 percent of their personnel expenses
• 16 percent of data processing expenses
• 20 percent of legal expenses
• 38 percent of accounting and auditing expenses
• 48 percent of consulting expenses

Other than the high costs incurred by banks to stay compliant, the frequency of reporting and the increase in regulatory requirements add to the pressure. Banks have to face annual regulatory tests along with submitting quarterly call reports. The process to generate, format and scrutinize for any errors takes weeks and at times months. An example of this is the call report, a key quarterly filing required by regulators. It now has 57 rows and 89 pages of instructions, a huge pike in recent times.

Image From: CrowdFund Insider: Dallas Fed on Community Banks: “Too Small to Succeed”, Crushed by Excessive Regulations

Although regulators agree with the need to change the outlook towards regulating small and medium size banks, the steps toward regulatory relief might take some time to be enacted. “We understand a one-size-fits-all approach doesn’t work,” said OCC Senior Deputy Comptroller for Midsize and Community Bank Supervision Toney Bland. Bland acknowledged that his agency “could do more” to reduce regulatory burdens on smaller institutions and said the OCC was in the midst of a comprehensive review of its regulations toward that end.[6]

A data-driven solution will provide consistency and transparency throughout the entire analytics and submission process and will strengthen banks’ risk data aggregation capabilities and internal risk reporting practices. -Alex Tsigutkin

Banks need to rely on technology and enhancing and simplifying existing processes to streamline and meet regulatory demands. In order to do so, banks need to prioritize and strategize their objectives when it comes to addressing data challenges.

Hexanika: The end-to-end regulatory solution

Hexanika is a FinTech Big Data software company which has developed an end-to-end regulatory compliance solution. The innovative machine learning big data solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

Hexanika helps establish a compliance platform that streamlines the process of data integration, analytics and reporting. The software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. The solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

To know more about our products and solutions, read: http://hexanika.com/company-profile/

Author: Yogesh Pandit

Contributor: Vedvrat Shikarpur

Feature Image Credits: Skyangel

[1] Working Paper: How are small banks faring under Dodd-Frank?

[2] St Louis Fed: Compliance Costs Community Banks $4.5 Billion Annually, Survey Shows

[3]Tanya Marsh and Joseph Norman, “The Impact of Dodd-Frank on Community Banks,” American Enterprise Institute, white paper, 2013, p. 7, at https://www.stlouisfed.org/banking/community-banking-conference/PDF/Marsh_Norman_Reforming_Regulation.pdf

[4]Gov. Daniel Tarullo, “Rethinking the Aims of Prudential Regulation,” speech at the Federal Reserve Bank of Chicago Bank Structure Conference, May 8, 2014, at http://www.federalreserve.gov/newsevents/speech/tarullo20140508a.htm

[5] Law 360: Small Banks Slam ‘One Size Fits All’ Dodd-Frank Regs

[6] Law 360: Small Banks Slam ‘One Size Fits All’ Dodd-Frank Regs

To provide the context necessary to understand the regulatory costs on small banks, let us first have a look at the definition of a small bank.

What is a Small Bank?

Banks are typically classified as small or large based on their total asset size (i.e., the value of the loans and securities they hold), but there is no standard, commonly accepted threshold for what asset size constitutes a small bank. Some researchers define small as $1 billion or less in assets, whereas others define it as $10 billion or less. The Federal Reserve defines small & mid-sized banks as those banks with less than $10 billion in assets. Often, the term community bank is used as a synonym for small bank. The Office of the Comptroller of the Currency (OCC) defines community banks as generally having $1 billion or less in assets.

The terms small bank and mid-sized bank as commonly used encompasses a disparate group of institutions, varying in size, activities, and charter. Asset size is not the most intuitive concept to understand what is meant by a small bank. To provide some additional perspective on the size of small institutions, it is informative to look at the number of employees at institutions of different asset sizes. At the end of 2014[3], a bank with approximately:

1. $100 million in assets had, on an average, 25 employees.
2. $1 billion in assets had, on an average, 214 employees.
3. $10 billion in assets had, on an average, 1173 employees.

What is Regulatory Reporting Burden?

Financial regulation reporting can result in both gains and costs. The cost associated with government regulation and its implementation is referred to as regulatory burden. In the banking world, regulatory burden can be borne by banks, consumers, the government, and the economy at large. A bank may have to face higher costs because it now must train its staff on how to properly apply the rules and may spend more time reviewing each application. Some of this cost may be passed on to consumers through higher interest rates and fees or fewer lending options.

Regulatory burden on banks is manifested primarily in two different ways: operating costs and opportunity costs.

1. Operating costs (or compliance costs) are the costs the bank must bear in order to comply with regulation. For example, in response to a new rule, a bank may spend more money training its employees to ensure they understand the new rules, and the bank may have to purchase updated computer programs because the new rule defines concepts in ways that are incompatible with its old systems. Updating computer programs is an example of operating costs that are one-time costs borne upfront. Other costs, such as hiring additional compliance officers, are recurring costs that exist so long as the requirement is in effect.
2. Opportunity costs are the costs associated with foregone business opportunities because of the additional regulation. A bank may, for example, offer fewer mortgages because new regulations make mortgage lending more expensive and instead choose to perform a different type of activity that is now more profitable.

Characteristics Determining the Regulatory Reporting Burden a Small & Mid-Sized Bank Faces:

Size is one of the several factors that influences burden. The regulatory burden borne by a bank depends on what rules are applied to it (rulemaking) and how those rules are applied (supervision and enforcement). The factors that determine what rules are applied and how are as follows[4]:

1. Charter- Because a bank’s primary regulator depends on its charter, to the extent that different bank regulators have different practices and policies, the charter will influence the regulatory burden. If the bank has a state charter, supervisory examinations can alternate between the state banking regulation and its primary federal regulator. Differences in regulation and regulatory burden between banks and credit unions are a perennial concern to the banks.
2. Risk profile- Not all banks pose the same risk of failure, risk to consumers, and risk to the overall system, so policymakers tailor some regulations and supervisory practices by risk profile in order to reduce regulatory burden. For, example, because small banks with higher supervisory ratings- signaling that they are perceived to be healthier- are, all else equal, less likely to fail they are examined less frequently and intensely than banks with lower ratings.
3. Business model- Certain activities will attract more regulatory scrutiny than others because they are riskier, more complex, pose more risk to consumers or broader economy, and so on. Different banks offer different types of services and engage in different types of activities, therefore some banks will have greater regulatory compliance costs because they are involved in more activities or lines of businesses that require oversight. In other words, the same activity will require a certain amount of regulatory compliance at any bank that undertakes it. Notably, if banks engage in certain activities such as operating in the securities or derivatives market, it could trigger additional activity-based regulation

Hexanika: Compliance Made Easy

Hexanika is a FinTech Big Data software company, which has developed an end to end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance.

Hexanika helps establish a compliance platform that streamlines the process of data integration, analytics and reporting. Our software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

To know more about our products and solutions, read: http://hexanika.com/company-profile/

Contributor: Akash Marathe

Image Credits: www.dailydot.com

[1] Source: http://www.dispatch.com/content/stories/business/2016/03/25/1-regulatory-costs-hurting-small-banks.html

[2] Source: http://blogs.wsj.com/moneybeat/2014/07/30/the-cost-of-new-banking-regulation-70-2-billion/#:t3lnbKjgIaBajA

[3] Source: https://www.fas.org/sgp/crs/misc/R43999.pdf

[4] Source: https://www.communitybanking.org/…/Session3_Paper3_Cyree.pdf

About the author

Until 2014, Prakash Jalihal served as Managing Director and senior vice president of Fidelity Information Services (FIS) for Latin America and the Caribbean and was responsible for the direct management of sales and operations for the company’s business in that region The company now operates in over 30 countries in the region and is rated #1 Financial Technology company by FinTech.

Jalihal began his career with FIS, formerly Alltel Information Services and formerly Systematics in 1972. Prior to his current position, he has held various positions: Programmer/analyst, Systems Development Division manager, Planning and Research manager, Little Rock Processing Center manager, Global Client Operations manager.

Before joining Fidelity Information Services, Jalihal was a production control engineer with International Harvester Company in Bombay, India.

He earned a Bachelor of Science in Mechanical Engineering from the University of Bombay in 1970 and a Master of Science in Industrial Engineering from the University of Arkansas in 1972.

From 1995 through 2001, Mr. Jalihal served on the Global Advisory Board for the Thunderbird School for International Management and is currently on Dean’s Advisory Council for the University of Arkansas Engineering School.

Mr. Jalihal currently serves on the Board of EAST Initiative, a non-profit organization established to promote Environmental and Spatial Technology in schools in Arkansas and surrounding states, based on project based learning. He also serves on the Advisory Board of the non-profit Best Buddies Arkansas.

In 2009, he was inducted into the Arkansas Academy of Computing and in 2011, he was inducted into Arkansas Academy for Industrial Engineers.

In 2014, Mr. Jalihal joined the Global Technology Deployment Initiative as a Senior Advisor. The charter of GTDI is to provide funding and deploy technology in the countries that lack funding and do not have a technology distribution channel.

In 2014, he was also elected as a member of Arkansas Accelerator, an economic development advocacy group that promotes creation of high paying jobs and formation of new companies as part of a knowledge based economy.

Originally published at hexanika.com on May 9, 2016.

Regulators demand information on various activities (liquidity management, asset liability, foreign exchange, risk management, transactional data, and more) to analyze an organization’s financial health. This process is implemented from time to time and requires data to be collected and processed in different formats as per the requirements of the regulator.

Although this reporting process seems relatively easy, it starts getting complex as regulatory requirements differ across regions, also changing according to which regulatory body the bank is reporting to. With the evolving regulatory environment and the introduction of new rules (BCBS 239, MiFID II, Basel I, II, III, Volcker Rule, etc.), banks need to streamline their process of information gathering, calculations and analysis, reconciliations and auditing, generation of reports, and so on. To do this, financial institutions and banks must implement robust processes and systems.

Technology is one of the key challenges for banks to get their reporting process streamlined. Here is a look at the key technology challenges for banks and why a solution that automates and streamlines the regulatory reporting process to generate accurate reports in a timely fashion is the need of the hour.

What are the challenges? [1]

Credits: EY Report on Regulatory Reporting

According to the 2014 Federal Reserve regulatory reporting survey conducted by EY, two of the greatest challenges faced by financial firms are:

• Managing Data Quality
• Maintaining Data Granularity

System Limitation and Data Integrity were cited as the major challenges leading to inaccuracies in data quality and inconsistency in maintaining data granularity. It is noted that data integrity issues increased from 65% in 2012 to 72% in 2015.

It was also found that banks and financial institutions were not spending adequate time on performing analytics and review process. Reporting analytics is integral as it assesses accuracy and completeness of information before it is sent to regulators. It validates that reported data is an accurate representation of the institution’s financial position. Despite the Federal Reserve asking organizations to spend 80% of their time on analytics, banks are not doing so.

The findings show that more than 68% of the banks spent more than 50% of their time on preparing regulatory reports, while 20% managed to dedicate 50% of their time to analytics, review and sign-offs. Only 12% could comply with the Fed’s suggestions.

Credits: EY Report on Regulatory Reporting

Banks are adopting many more analytical procedures to maintain internal controls on their data. Variance analysis, trend analysis, ratio analysis, and other analytical processes are being implemented to ensure data accuracy and completeness. However, there are still various banks that do not have adequate technology in place to perform analytics easily.

Financial institutions have also begun to understand the importance of automation to reduce manual intervention. Even today, a high number of banks still perform a large volume of manual adjustments to provide granular financial data. 75% of firms surveyed have developed high-level plans to automate recurring adjustments. Automation significantly reduces the time and resources required to perform recurring tasks,

The findings point to the following key changes that banks need to make:

1. Streamline Regulatory Reporting Process.
2. Dedicate additional resources and time to analyze data.
3. Automate recurring processes to simplify report generation processes.
4. Upgrade existing data warehouse platforms to handle and store larger volumes of data effectively and efficiently.
5. Manage data from multiple sources by shifting to centralized data sources.

What are the reasons behind these challenges?[2]

Existing IT systems at most banks and financial institutions are finding the dynamic regulatory environment challenging, most finding it hard to keep pace with the new requirements and demands. Here are some of the key reasons why the regulatory reporting process is complex:

Multiple data sources

For reporting, banks compile heterogeneous data that comes multiple data sources, e.g. market/risk data, finance data, reference data, transactional data, etc. Banks need to collate this information to meet reporting requirements, requiring development of IT applications that are equipped to handle large influx of data from various data sources. This impacts time and cost required to generate reports.

Data storage complexity

The large influx of data that comes in is stored in different systems, depending on the type of data. As banks exchange information through financial statements, risk reports, capital submissions and other regulatory reports, this information needs to be effectively crosschecked and analyzed for ensuring data accuracy. This required an effective reconciliation process to be in place, to ensure data is accurate across systems.

Multiple report formats

Reporting formats vary depending on the regulator, regulatory requirement, report type and geography of the bank where the report is to be submitted. Most organizational systems are not flexible enough to modify existing data model to meet changing requirements. It requires complex coding to be fed in the system to adapt to the new requirements, which keep changing periodically.

Strict timelines and resources

Regulators require financial institutions to alter their internal process and meet reporting timelines, failing which they are severely fined. Though information of implementation dates and reporting deadlines are shared beforehand, most banks are under stress to meet these deadlines as the systems in place are rigid and require complex coding and resources to be modified.

To do this, banks not only require skilled financial experts but also professional coders who are in sync in mapping business requirements in existing IT infrastructure. Developing new applications and tweaking processes that are already in place is time-consuming and requires frequent changes.

Why choose Hexanika?

Hexanika is a FinTech Big Data software company which has developed an end-to-end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance. Our innovative solution improves data quality, keeps regulatory reporting in harmony with the dynamic regulatory requirements and keeps pace with the new developments and latest regulatory updates.

Hexanika helps establish a compliance platform that streamlines the process of data integration, analytics and reporting. Our software platform can develop and clean data to be sourced for reporting and automation, simplifying the processes of data governance and generating timely and accurate reports to be submitted to regulators in the correct formats. Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

To know more about our products and solutions, read: http://hexanika.com/company-profile/

Contributor: Vedvrat Shikarpur

Image Credits: Tech in Asia using Creative Commons License

Related Articles

Footnotes:

[1] EY: Regulation now: the new standard and how firms are adapting

[2] TCS: Regulatory Reporting: What Banks can do to Keep Pace with the Changes

Originally published at hexanika.com on April 30, 2016.

We take a look at OATS reporting requirements, the challenges financial firms face to comply with them and how they can be addressed.

What is OATS?

FINRA established Order Audit Trail System (OATS), an integrated audit trail of order, quote and trade information for all NMS (National Market System) stocks and OTC equity securities. OATS requires electronic auditing and reporting capabilities on all stock and equity orders, quotes, trades and cancellations. The rules for OATS were approved by the SEC on March 6, 1998. FINRA uses this audit trail system to recreate events in the life cycle of orders and more efficiently monitor trading practices of member firms.[1]

OATS requires time sensitive information related to events throughout the order execution process to be recorded accurately. According to Rule 7430 (updated from NASD Rule 6953), all computer clocks and timestamping devices must be synchronized to be with reference to a time source as designated by FINRA. This data can be collected during the day and transmitted to OATS in one or more files at a convenient time; however, reports for events that occur during particular OATS Business Days must be reported by 5am EST the following calendar day.[2]

Why is it challenging for banks?

FINRA fined Goldman Sachs Execution & Clearing $1.8 million on July 27, 2015 for systemic OATS reporting violations spanning a period of 8 years, including failure to accurately submit trade reports to FINRA Trade Reporting Facility. FINRA rules mandate firms to transmit applicable data for OATS in a complete and accurate manner.[3] Similar fines were previously levied in 2008 to three firms for not having adequate systems of supervision in place to monitor OATS reporting compliance. The total fines for these firms came to about $1.6 million.[4]

Similarly, financial firms that are under the purview of FINRA with OATS applicable compliance requirements face issues due to various technology and regulatory complexities. Some of these challenges are as follows:

1. Data inaccuracy and reporting complexities

On February 27, 2014, SEC approved FINRA’s proposed rule requiring firms to express time in milliseconds when reporting order information for OATS if firm’s system are capable of capturing time in milliseconds. This amendment came into effective from April 7, 2014.[5]

This create large complexities as firms are required to record order event times in hours, minutes, seconds and milliseconds (HH:MM:SS:mmm). This requirement means firms now have to record and store data for every order with more values and report it within the stipulated timeframe. The Head of Enterprise Architecture at NYSE described the problem as such:

“When you’re talking about billions of transactions per day, building systems that can take unfriendly data and turn it into regulation-friendly, analysis-ready information is a key, ongoing struggle… We are obligated to maintain data [for seven years]. There [was] not one system out there that could actually store that data and have it online.”[6]

Thus, OATS is complex and costly because it requires data to be found, collected, transformed, stored and reported on-demand from a variety of sources and data formats with relatively short timelines, eventually leading to issues like data inaccuracies.

2. Data storage and Real-time Monitoring

OATS requires various data elements such as: account type code, limit order display indicator, limit price, routing firm MP ID, routed order ID, etc. to be reported in a timely manner comprising of complete and accurate information. [7] To ensure this information is proper, firms need to match and compare Order/Trade Rates, crosscheck the reported Routed Order ID is correct with accurate Electronic Synchronized Timestamps, and other analytical operations.

This requires all transactional and financial documentation to be stored and maintained for at least three years, many a times requiring a longer shelf life. Records must be readily searchable and retrievable to ensure smooth and timely functioning of the analytical operations.[8]

Other than having capable systems that can efficiently store large amounts of data, FINRA also requires for proper supervision of records to ensure there are no lapses.

3. Diverse Markets and Trading Systems

Complications arise when firms look to diversify their business model to take advantage of various trading systems and execution venues. No trading systems or markets are alike, each having their own strengths and weaknesses.

FINRA requires a single record for each order; from when order was received, where it was routed, when it was routed to liquidity destination, when execution came back and whether there was a cancellation/replacement. When each system is left to report OATS individually, the consolidated OATS records and the mix-and-match approach leads to several issues.

4. Changing requirements and high costs

As FINRA keeps issuing various updates to streamline the existing process of OATS reporting to be more precise, firms faced with complex and frequently changing reporting environment prefer to go with a single system approach. Although it eliminates the issues that crop up due to diversification, the approach is expensive and challenging for existing business models that are in place. [9]

How does Hexanika help?

Hexanika is a FinTech Big Data software company, which has developed an end to end solution for financial institutions to address data sourcing and reporting challenges for regulatory compliance.

Our software platform streamlines the process of data integration, analytics and reporting by cleaning and joining the sourced data through semantics and machine learning algorithms. This ensures OATS applicable data is securely stored in the cloud and is easily retrievable using Hadoop architecture.

Our solution also simplifies data governance process and generates timely and accurate reports to be submitted to regulators in the correct formats. Our solutions also significantly reduce the time and resources required for everyday-regulatory processes, and are robust enough to be implemented on existing systems without requiring any specific architectural changes.

This provides an accurate OATS report with the option to analyze the data in using business Intelligence for analytics and tracking record changes.

Contributor: Vedvrat Shikarpur

Image Credits: 401(K) 2012 via Creative Commons (Website: http://401kcalculator.org)

[1] FINRA OATS page

[2] FINRA OATS Technical FAQs: Q T10

[3] Valuewalk: FINRA Fines Goldman Sachs $1.8 Million For OATS Reporting Violations

[4] FINRA: FINRA Fines Three Firms a Total of $1.6 Million for OATS Reporting and Supervision Violations

[5] FINRA: Firms Capturing Time in Milliseconds Required to Report to OATS in Milliseconds Beginning April 7, 2014

[6] Cloudera: Full-Fidelity Analytics and Regulatory Compliance in Financial Services

[7] FINRA: OATS Report — November 2, 1999

[8] FINRA OATS FAQs: Q C8

[9] SS&C: Can you meet your OATS Reporting Requirements?

Originally published at hexanika.com on April 15, 2016.