November has been another productive month and we have a lot to share with you about our FusionFabric.cloud platform and our global hackathon, Hack To The Future.

Five new applications are now officially available in our store

• Dinero Cash — Nextgen mWallet — fully integrated with the core banking system, a bank can now easily deploy a mobile wallet ecosystem integrating third party players into the platform, facilitating the lives of millions of unbanked and enhancing banked users with a mobile wallet companion.
• Detech Optimizer — sophisticated next-generation decision-support tool for strategic balance sheet and risk management in financial institutions. The tool optimizes assets, liabilities and capital for long-term strategies and prepares decision-makers for numerous future scenarios simultaneously.
• SCR Optimizer — with this application, you can define, compare and apply optimization strategies on insurance portfolios in a Solvency II regulatory context. Portfolio managers can control the SCR of the portfolios at any time.
• Payment Fraud Prevention — NetGuardians’ AI platform detects fraudulent transactions in real-time, reduces the number of false positives by 83%, fraud management time by 93%, discovers new fraud types, and reduces fraud losses.
• Toniqs — this app enables you to deploy Neo Banking on Demand with Toniqs Cloud connected to Finastra.

Finastra APIs

• New Service API — Financial Institution Identifications
  Services related to retrieval of financial directory information based on their identifiers.
• New API version — Real-Time Payment Initiation
  Finastra Open API Specifications for Real-Time Payment Initiation API.

REGISTER to ‘Hack To The Future’: already 4,000+ participants from 80+ countries

Be a part of what we hope will be the most impactful fintech hackathon ever, with #HackToTheFuture 2020! Build your app with Finastra APIs to accelerate transformation, because we are #hackingforgood!

You have until December 20th to submit your apps and help us to redefine finance for good!

Register now!

If you have questions or need help, contact our dedicated team on the Community Q&A page.

Last but not least, we invite you to check our latest platform-related content HERE.

Many thanks,

FusionFabric.cloud Team

ABOUT FUSIONFABRIC.CLOUD

FusionFabric.cloud is Finastra’s open and collaborative developer platform and marketplace for financial apps. Access banking systems and data across: retail banking, payments, lending, corporate banking, treasury and capital markets via APIs.

Find out more here 🚀

FusionFabric.cloud: November Newsletter was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

October has been an exciting month and we have a lot to share with you: from our brand new data-focused offering and new apps in our store to an update on our global hackathon which we invite you to join so we can redefine finance for good together.

Fusion Data Cloud: data ecosystem + actionable insights + connected experiences

We have just launched Fusion Data Cloud which enables fintechs to collaborate with and promote new, innovative solutions to thousands of Finastra customers, speeds time to market and provides major scale when compared to going solo.

Two new applications are now available in our store

• Floatbot : Finbot for RetailFinbot — Floatbot is a SaaS-based conversational AI platform that helps banks and Fintechs to automate customer support, increase customer experience and digital sales. Its DIY “No Code” platform makes it possible to build hybrid bots using advance workflow-builder and conversational AI tools.
• AIO — Verified Financial Identity — AIO is driving trust between businesses, enabling customers to establish and own their verified financial identity, and to share it securely with financial institutions.

Finastra APIs

We invite you to check our latest API-related updates.

REGISTER to Hack To The Future: already 3,400+ participants from 70+ countries

Be a part of what we hope will be the most impactful fintech hackathon ever, with #HackToTheFuture 2020! Build your app with Finastra APIs to accelerate transformation, because we’re #hackingforgood!

We are delighted to generate so much enthusiasm and drive such a tremendous level of participation just under two weeks following our official launch.

Did you know that 40+ partners and sponsors joined us to offer collectively over $90,000 in prizes?

You have until December 20th to submit your apps and help us to redefine finance for good! Come and register here.

If you have questions or need help, contact our dedicated team on the Community Q&A page.

Many thanks!

FusionFabric.cloud Team

ABOUT FUSIONFABRIC.CLOUD

FusionFabric.cloud is Finastra’s open and collaborative developer platform and marketplace for financial apps. Access banking systems and data across: retail banking, payments, lending, corporate banking, treasury and capital markets via APIs.

Find out more here 🚀

FusionFabric.cloud: October Newsletter was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s all about building the future!

This month we are taking a break from talking about new features and new APIs, to focus on existing and upcoming opportunities to build with FusionFabric.cloud APIs.

The Open Finance Appathon is now in full swing. We are challenging fintechs to create market-ready solutions across:

- Retail and SME Banking

- Payments

- Corporate Banking

- And Open Finance for Good

This is a unique opportunity to get extra support, attention and prizes to integrate with Finastra’s FusionFabric.cloud APIs. Last year we had amazing fintechs in North America take part in our first Appathon and they’ve proceeded to sell their applications to our community bank and credit union clients.

Now is your chance to take part in this bigger Appathon! Watch one of the livestream sessions to hear from the judges and join the Finastra API deep dive.

Watch the livestream sessions here.

Back for the sequel: Hack to the Future!

After having over 1000 developers take part in Hack to the Future 2019, the sequel is coming very soon and we’re hacking for good.

The pandemic has intensified some of the world’s most challenging issues — poverty, inequality and exclusion to name but a few. Digital infrastructure and technology are providing the platform to transform access to financial services for historically excluded populations and create better, fairer and more inclusive outcomes, and institutional opportunities for efficiency, growth and diversity.

Hack to the Future 2020 is focused on creating a brighter future that supports every person, business and community, through technology. It’s open to all: students, fintechs, banks, even children!

Find out more and register your interest.

If you have questions or need help, contact our dedicated team on the Community Q&A page.

Many thanks,

FusionFabric.cloud Team

ABOUT FUSIONFABRIC.CLOUD

FusionFabric.cloud is Finastra’s open and collaborative developer platform and marketplace for financial apps. Access banking systems and data across: retail banking, payments, lending, corporate banking, treasury and capital markets via APIs.

Find out more here 🚀

FusionFabric.cloud: September newsletter was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

It’s here…dark mode!

As we continue to add more functionality to Finastra’s developer portal, we are also building some fun UX features! No developer portal is complete without a dark mode, so we’ve added one. To enable dark mode, log in and navigate to ‘user settings’ in the top right corner. There you will find the preference to enable dark mode.

NEW COMPETITION — Open Finance Appathon!

Calling on all fintechs across the globe with a product or service built for the European market! We challenge you to use the power of APIs from Finastra, Railsbank and Yolt Technology Services to scale your existing proposition or develop new ones in the following categories:

· Retail and SME Banking

· Payments

· Corporate Banking

· Open Finance for Good

Supported by the world’s leading industry community, Open Banking Excellence, this competition is a unique chance to lead the pack of open finance solutions with the potential to transform financial services that are being developed across the globe. It is also the perfect platform for integrating existing solutions or those that pass proof of concept stage.

Find out more here.

Data classification added to APIs

Finastra has a wide range of requirements to protect the confidentiality, integrity and availability of data under our custody. To protect data, we apply safeguards and controls which are appropriate given the sensitivity and criticality of the data.

There are two levels of classification of data:

1. Public data (for example market data)

2. Restricted data (information that is protected under GDPR or CCPA)

The data classification tagging applies both to API level and to endpoint level. The tag is included in each API reference documentation and can be found in the OpenAPI specification.

Read more about data classification here.

And if you have questions or need help, contact our dedicated team on the Community Q&A page.

Many thanks,

FusionFabric.cloud Team

ABOUT FUSIONFABRIC.CLOUD

FusionFabric.cloud is Finastra’s open and collaborative developer platform and marketplace for financial apps. Access banking systems and data across: retail banking, payments, lending, corporate banking, treasury and capital markets via APIs.

Find out more here 🚀

FusionFabric.cloud: August newsletter was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

This blog aims at simplifying the terminologies and operation of a Blockchain and serves as a platform to understand the basic usage of Blockchain in Transaction Banking.

What is a Blockchain?

A lot of my friends and colleagues have recently asked me to explain them Blockchain in the simplest way possible. So, let us start with a basic definition of Blockchain.

“Blockchain, simply put, is a chain of records which are called blocks that are linked to each other with secured encryption methods. These blocks keep on growing while maintaining cryptographic information, timestamp and actual data. This feature makes the Blockchain a series of immutable records”

A Blockchain at an initial stage is a closed group of participants who agree to share data which is always transparent and known to all of them. These participants share the information on the network which is accessible by all the other participants at the same time. Thus, making Blockchain a collection or database of information chunks or blocks which are linked to each other in a secured manner. Information recorded can be of any form; It can be a simple money transfer (transaction) between two parties, an agreement for goods transfer between 2 parties, addition of police records for citizens, transfer of titles or even as small as information about one particular flavour of Ice cream for a multinational dairy chain. So, what distinguishes Blockchain from the traditional data management systems? This can be easily understood with the following characteristics of a Blockchain:

1. Information is always added with Secured Hash (linked to the previous block), Unique identity and Timestamp.
2. Any participant on the Blockchain can view the ledger (or blocks) of transactions.
3. Information added cannot be altered by anyone at any point of time.

Now that we have defined a Blockchain and we are “somewhat” aware of the basic terms, let us move on to understand how it is useful in the world of financial payments.

Blockchain for Cross Border payments

Financial data is considered to be one of the most valuable data in the world. We don’t want our bank balances to be scattered over internet, do we? One such key application in Financial services that can prove to be a game changer if utilizing Blockchain is — Transaction Banking or simply put Payment transfers.

Current system:

Traditional cross border payments usually take 1 or more days to finally credit the beneficiary customer since it involves several intermediary agents. With Blockchain — it can be achieved in seconds!

Payments on Blockchain:

With Blockchain, each participant will be connected to the Blockchain network and will be able to connect/talk to every other participant.

Now let us go a little bit further and understand how a payment will be processed via Blockchain and how it will be different to the traditional approach. In this step we will also try to shed some light on the term “Distributed Ledger”.

Distributed Ledger refers to secured, replicated and synchronized digital data that is spread across geographies (multiples countries, sites or institutions). In simple terms it means everyone on the network has a real time copy of the data and is able to witness any change on the data instantly. When distributed ledger term applies to transaction baking or payments — it refers to information related to accounts, account balances and account transactions. Now it will make more sense to imagine how a Blockchain can help achieve a distributed ledger. DLT — Distributed Ledger Technology.

How a payment would be done using Blockchain/DLT?

Let us take a simple example where Jenny form France is trying to send 1000,00 EUR from BNP Paribas, Paris to Alex who has an USD account with Bank of America, New York Branch. Using a traditional approach, this payment would ideally be sent by BNPP Paris to its Correspondent in USA which would then forward this payment BoFA via the Fedwire/CHIPS system. Thus, the time taken for settling this payment would indeed be affected by the multiple hops involved in the process.

Switching on to a Blockchain, where both BNP Paribas and BoFA are part of the Secured Blockchain and hold a Distributed ledger, its just a matter of new transaction being added to the chain. Now, as Jenny requests BNP for the transfer, BNP creates a record of this transaction which has details like:

1. Timestamp
2. Debit account
3. Credit account
4. Sending bank
5. Amount
6. Currency
7. Receiving bank

and places this record on to the chain with required encryption (hash). As soon as the record is added on to the chain, its validated by each node (participant) with their key for integrity and the block is finalized. Block now contains the information that is accessible by all participants and denotes that Jenny from BNP Paris, is trying to send money to Alex who has account with BoFA. In turn the receiving bank — BoFA instantly initiates the accounting at their end where in customer account is credited with the required amount. Thus, finalizing this payment within 5–6 seconds.

After a block is added, the immutable property of a Blockchain makes it impossible to alter any information on the block thus making it highly secured and uncompromised.

Talking about robustness, a Blockchain relies heavily on the underlying algorithm, which is basically the foundation that controls the complete functioning. There are a variety of different algorithms currently in market that help us build a robust Blockchain. Consequently, making a Blockchain capable of handling huge volumes easily with 100% accuracy — all this in a secured environment. Some key advantages of using Blockchain for payments are listed below:

Advantages of using Blockchain for payments

1. Real-time — Payment transfers are completed within seconds, in turn helping the beneficiaries with early access and better utilization of funds.
2. Low cost — Reduction in number of intermediaries reduces the Fees applied on the payment and hence is beneficial to the customers and Financial institutes.
3. Secured — Immutable nature of Blockchain makes the transaction tamper proof- eliminating the risk of any cyber-crime.
4. Unambiguous — Since Blockchain stores unique records, there is no ambiguity or duplicate data present on the chain.
5. Transparent — One of the key advantages which enables all the participants to view all the records without any concealment of information.

Current market status:

Blockchain for payments adoption has already begun with a lot of FinTechs jumping the DLT bandwagon. IBM has already introduced a Cross-Border payments solution using Stellar Protocol, called as IBM WorldWire. Major banks like Bank of America, HSBC, RBC, Rabobank, ANZ, SBI and Barclays have already planned/begun initial trials of their Blockchain based payment systems. Tech companies like Ripple Labs Inc. have already placed their Ripple protocol-based payment RTGS systems in the market. Ripple makes use of their cryptocurrency XRP to achieve a decentralized ledger.

Thus, looking at the immense potential that Blockchain has, it becomes imperative to include more and more use case for its application in different work areas. Feel free to share the different scenarios that you think can really be a great use cases for Blockchain based usage in the comments section. Also, feel free to connect with me for any review comments, queries or any additional discussions that you would like to have regarding payments and Blockchain.

Blockchain for Payments was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Fans of machine learning suggest it as a possible solution for everything. From customer service to finding tumours, any industry in which big data can be easily accessed, analyzed and organized is ripe for bringing about new and compelling use cases. This is especially attractive for legacy organizations, such as financial services firms, looking to gain an advantage.

These businesses are usually well embedded in their markets, fighting with competitors over small margins and looking for new ways to innovate and drive efficiency. They also have an abundance of historical and contemporary data to exploit. One asset any start-up lacks is owned historical data, which gives legacy firms an edge in the competitive landscape. The promise of machine learning is therefore particularly seductive — feed in your extensive customer and business insights along with your desired outcome and let algorithms work out the best path forward.

However, established businesses such as these are also the ones that can face the biggest challenges in driving value through machine learning due to technical debt, poor infrastructure and low-quality data, leading to higher costs of deployment as well as higher maintenance costs.

Take a legacy financial institution as an example. Though the organization may have extensive historical data, much of it may be held in old documents and unstructured formats. Without effective data mining capabilities, both in terms of expertise and technology, this data will remain largely unusable. It’s only when dedicated data science teams and tools are put to work that this value can be unlocked.

At a recent developer meetup, I heard from Teodor Popescu, a Data Engineer at BBC about how he deals with these issues for the nation’s biggest broadcaster.

Too much of a good thing

“There is so much hype about machine learning, but no one talks about the infrastructure behind it,” explained Teodor.

In every machine learning project, the raw material is high quality data. In legacy businesses, while there may be a lot of data around, it is often unstructured, incomplete or hard to find. IBM confirms that for most companies, 80 percent of a data scientist’s time is spent simply finding, cleansing, and organizing data, leaving only 20 percent to actually perform analysis and to run the clean data through a model.

Large volumes also lead to issues with scaling, as Teodor found when training a machine learning model on three billion data points. Infrastructure struggles to keep up with the volume of information, while processes that deploy and track the results need to be scaled at the same time.

The power of pipelines

At the BBC, there are over 1.3bn events generated every day. This requires machine learning teams to focus a lot of their time finding, maintaining and expanding sources of reliable data.

By working with third party integrations, teams can mitigate some of the existing issues around data management by sourcing new, structured data. However, these integrations still require maintenance, with broken pipelines slowing down development and deployment.

Instead of focusing solely on how to bring more data in, organizations can instead focus on the infrastructure for managing the data internally.

Tailoring systems

There are two approaches to this problem: specific data infrastructure and specialized team structure.

One example is personalization. In order to maximize speed, click data from iPlayer is channeled through a distributed streaming service (such as Apache Kafka, Amazon Kinesis or a collection of Amazon SQS services) and processor Apache Spark in sequence, before being delivered to storage in AWS or back to iPlayer, via API, so that personalized options can be presented back to the user.

This is also reflected in the way of structuring data science teams, introducing DataOps and MLOps to take on specific roles.

These teams work behind the scenes to enable better performance across the data science teams, focusing on the robust implementation of data version control, ensuring adequate testing is conducted for both models and the data; working to accelerate the journey to machine learning deployment and reproducibility.

Given the specialized nature of many of the systems, developers can play a key role in determining what is possible, efficient and valuable for the organization. Legacy organizations looking to leverage their data therefore need to focus on specific issues and datasets in order to deliver targeted solutions efficiently, rather than taking a broad approach. Machine learning models are only as good as the data that feeds them, so establishing the use case for specific datasets is vital.

Finding the right problems

Despite the difficulties, machine learning can still be an incredibly valuable tool for legacy businesses. The key to success is tailoring your approach to data and tools to the specific needs of the organization.

The goal for internal development and data science teams in this process is to align the business on goals, methods and infrastructure. In essence, fully understanding a business problem is the key to creating the right use cases for any data project. This is the only way to ensure robust processes once in production, while managing scope and efficiency. In this way, teams can build incremental gains that drive long-term value throughout the organization.

About the author

Ryan Clifford is the Developer Relations Lead for FusionFabric.cloud, Finastra’s open development platform solution. He is passionate about building the fintech and developer community and helping the ecosystem to connect into FusionFabric.cloud.

Machine Learning challenges in legacy organizations was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

The finance industry is a prime use case for machine learning, thanks to the abundant data sets, access to capital and strong incentive for efficiency and predicting future outcomes. While rule-based workflows are well embedded within the industry, many businesses are now turning to machine learning to automate the algorithm building process, especially when it comes to fintech.

As digital services become more widespread, financial organisations need to move beyond rule-based mechanisms and manual data analysis to ensure compliance, security and customer service. Machine learning is more scalable, flexible and reliable when implemented properly, but requires the right data to deliver actionable insights.

This is especially the case when it comes to making predictions about human behaviour. At a recent Finastra developer meetup, I heard from Ben Houghton, Head of Data Science for Barclays Payments, about his data approach and how he makes his algorithms think like a human.

The Human problem

“When I talk about my job people always assume that I spend all my time building algorithms, writing code and teaching machines how to think,” Ben explained. “Most of my time is actually spent on data, cleaning it and matching it, everything. That’s why we need to be creative with the data we put in.”

To explain the need for the right data, Ben used the example of buying a milkshake. If we want to build an algorithm to predict which people will buy milkshakes, we would have a lot of data we could feed into it. This might include customer demographics of those who have bought milkshakes before, the type of milkshake they bought, the flavour, the price and the quantity of units sold.

These are very logical data points to use but they might not be the most predictive since you’re not looking at the data your customer cares most about. Without knowing what data they use to make their decision, it’s harder for your algorithm to copy their logic.

The customer might be more concerned with the nutritional value of the milkshake, how long it takes to consume and how much packaging or inconvenience is involved. As for demographic information, it could be important to input the employment status of customers to find those with the disposable income for a milkshake.

As Ben remarked, “Getting your data right and choosing the right information to feed in is definitely the most important part of building your algorithm.”

Fintech machine learning in practice

One of the core uses of ML is in cross-selling: introducing new products to customers based on their previous behaviour. Using machine learning, you can look at data from previous cross sells to see who has taken out loans or credit cards in the past, create models and combine that with data on existing product affinity across their user base.

By looking at who previously purchased certain products, as well as their previous purchases, the Barclays Payments algorithm can predict which other customers are the most likely to be successful cross-sell opportunities — useful for marketing campaigns or planning new customer journeys.

Machine learning is also used to predict fraud. By looking at previous fraud attempts, they collect markers that indicate a fraudulent transaction, such as time, location, value and point of sale, to instantly flag suspicious behaviour to customers in real time. Success depends on the ability to match data points to customer activity to build tailored models of what ‘normal’ looks like.

The power of preparation

“We spend probably 15% of our time building algorithms that make generalisations. We spend a lot of time getting our project into shape, working out what the problem looks like, or whether ML is even the right tool for it,” explained Ben. “A huge amount of our time goes on data, extracting it from our systems, understanding what we’re working with and cleaning it to ensure we understand all of it.”

Machines can only think like a human if they’re using the same inputs a human would. Success depends on making the right decisions at the start of the project, narrowing your scope onto the right issues, problems and approaches.

For financial organisations looking to leverage their datasets, the first step will always be to shrink the view to focus in on the most personal elements to help machine learning algorithms to do the same.

About the author

Ryan Clifford is the Developer Relations Lead for FusionFabric.cloud, Finastra’s open development platform solution. He is passionate about building the fintech and developer community and helping the ecosystem to connect into FusionFabric.cloud.

How to make your ML algorithms think like a human was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Test market app-etite

Want to assess market demand for your app? App Lab is our dedicated section on FusionStore to showcase apps undergoing development to our client base of 8,500 financial institutions. Where we see positive market traction and demand from financial institutions, we will work with you to help fast-track your integrations and app launch.

To take part in the program, here are the steps to take:

Step 1: Create a new development app in FusionStore

Login to FusionStore, go to ‘My Apps’ and click ‘Add App’. Select ‘Development app’ from the drop down of App Types.

Note, when you are ready to launch your app for real, you can revisit this section and change from Development app to FusionFabric.cloud app.

Step 2: Provide basic app profile information

This includes: name of the app and author; description; summary; logo; screenshots and videos; categories; website URL.

Step 3: Finastra will review the app profile

Once reviewed by the Finastra team, the app will become visible on FusionStore

New Apps Live!

This month we welcome 2 new fintech apps to FusionStore:

Conpend helps banks to accelerate their trade finance business through automation.

I-exceed’s Appzillon corporate mobile banking app enables treasurers and finance heads to approve transactions on the go.

A peak inside Finastra’s AI lab

Finastra’s AI lab focuses on how artificial intelligence and machine learning is leveraged throughout products and solutions; and experiments with new techniques. A recent research project looks into how federated learning can be used to train a neural network model without holding client data physically in your hands. Using encryption, web sockets, and a few other tricks you can successfully access client data to train a model without ever seeing it. Adam Lieberman and Brendon Machado explained the concept in a recent meetup.

Watch the video to see a demo.

APIs delivering a new standard of customer service

With 35% of treasurers already participating in open banking ecosystem platforms, and a further 42% planning to do so within twelve months. It is a clear sign to banks that they are reaching a critical window of opportunity to roll out open banking and API innovation.

Our white paper explores new business models, challenges, accelerators and what corporate banks should do today, to best future-proof for tomorrow.

Download white paper.

And if you have questions or need help, contact our dedicated team on the Community Q&A page.

Many thanks,

FusionFabric.cloud Team

ABOUT FUSIONFABRIC.CLOUD

FusionFabric.cloud is Finastra’s open and collaborative developer platform and marketplace for financial apps. Access banking systems and data across: retail banking, payments, lending, corporate banking, treasury and capital markets via APIs.

Find out more here 🚀

FusionFabric.cloud: July newsletter was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Finastra’s Developer Portal gives developers the power to use complex and specific financial functionalities to create applications.

In a previous article, we’ve detailed how to create an application using APIs only. Today, we’ll focus on the other building blocks that you can find in our Developer Portal. We fundamentally think that a Developer Portal should not only be an API Gateway. This is of course very useful, but it’s not enough. You need a platform !

These are the fundamental building blocks that we believe are necessary in a future-proof Developer Portal :

• APIs
• Events
• SPIs (Service Provider Interface)
• Data partnering

Let’s dive into each and explain why they matter !

APIs

No need to explain what an API is for developers reading this article. As for the rest of you, think of it as a request-response type of situation. Somebody asks for information, creates a request, and information comes out of the system through a response.

An example of that is Monotto, which created APIs on our Developer Portal for Fintechs to use. Have a look at their success story in this short video :

Monotto's RoboSave: Automating financial stability through artificial intelligence | FusionFabric.cloud

TLDR; APIs are the way to get informations out of a system and put informations into a system

Events

An event is kind of the opposite of an API in terms of flow, as it originates from a process that is running inside of the Bank’s core system.

Eventing is used to push information from the core banking system to anybody who is looking at that data. To be “looking at data” one would need to register/subscribe first to get notifications.

This eventing mechanism allows the subscriber to be informed in real time and do something about the event, but not necessarily to respond back to the bank.

Find an example below of an integration of events from FusionFabric.cloud, that was built on top of Salesforce Financial Services Cloud :

Fusion Credit Connect

TLDR; You need events because you cannot see, know or react to things that are happening in a system private to you

SPIs

SPIs inverse the relationship with APIs, but not the same way events do.

Basically, they allow for extension or change of behaviour of a system. Any of our product can define a place in their execution to call for an SPI. As a developer, you can provide an endpoint that you have developed to be requested upon the event is triggered.

Let’s take an simple example with Fraud Detection :

A developer has registered its SPI on a fraud detection event inside of a Payment Solution. A payment is being processed, and in place of using the core system’s way of checking for fraud detection, the bank is going to make a request to the SPI. In this way, the fraud provider would be able to return a fraud result, that the payment system would then use in the decision making in the rest of the process, ultimately leading to not processing the payment if the result leads to that conclusion !

TLDR; SPIs are the way to extend or change the behaviour of already existing APIs.

Data Partnering

Data partnering solves the issue of providing large amounts of data securely to data scientists, to enable them to train Machine Learning models.

As a data scientist, coming across meaningful volumes of data to be able to train models is very hard.

As a bank, your data is gold, and you cannot allow it to be publicly accessible to anyone, but you would benefit from outsiders’ Machine Learning algorithms.

Finastra is trying to bridge the gap between banks and data scientists through an API like contract, where data scientists can require access to Datasets, seeing the format, and a sample of the data beforehand, and Banks can grant consent on an individual basis. Once approved, the data flows in a protected way, being point to point encrypted. Finastra actually never sees this data.

TLDR; Data is mandatory to train a Machine Learning model

Those are the four building blocks with which you can basically build anything ! Let’s see some examples of how to combine all of these together to create a great customer experience !

Empathetic Banking

• Customer walks in branch.
• Cameras send an event to bank officer systems, retrieving customer’s history using APIs. AI systems retrieve data (Get Accounts) about the customer with insights on what products to offer customer. Trained from DataPartnering Data (Accounts, Transactions, etc…)
• Intelligence is gathered from external systems using SPIs (Account Lookup).
• After leaving the branch, IoT devices like the customer’s car and home provide follow-up by receiving events (Marketing) and invoking APIs (Offers).

Banking as a service

• Bank creates new revenue stream as the engine for non-financial company SolarWorld that embeds financial services in its consumer app.
• SolarWorld app does Loan Origination, KYC and Funding using APIs. SolarWorld provides fraud detection using SPIs in payment to make fast lending possible.
• SolarWorld app can tailor offering to consumer using AI-based predictions thanks to data and to geo-location and uses events to make them pop up.

That’s it for today !

I hope this was informative 🤗! If you want to go more in depth, go explore our Developer Portal :

FusionCreator Developer Portal

If you liked this article :

• Add some 👏
• Share it
• Follow our Medium Publications
• Try to make some apps using our Developer Portal & Design System
• … or only one of the above 😆

Till next time !

Finastra’s developer portal : More than just APIs was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Sometimes an idea is so good that everyone has it at once. One such idea is the ROSCA — a rotating savings and credit association — known as ekub in Ethiopia, cundinas in Mexico, tanomoshiko in Japan and dhukuti or dhikuti in Nepal. At a recent Finastra developer meetup we heard from Matthew Addison, CEO of StepLadder who has built a whole business on this concept, after writing his graduate thesis on the subject.

A form of peer-to-peer lending, the ROSCA relies on the idea of community — working with others to achieve financial goals, where the success of the group is tied to the success of the individuals within the group. Despite being an ancient concept, the strategy of community finance offers insight into how developers can structure applications to reduce financial risk.

Teamwork makes the dream work

The core idea of a ROSCA is that a group works together to set aside money and achieve their financial goals faster than they would alone. For example, 10 people who each needed to save £5,000 for a housing deposit would work together for a 10 month term, each contributing £500/ month. Each month, one member of the group would be selected at random to receive that month’s pot, instantly reaching their personal financial goal.

Eventually, every member will have reached their target. For most, this will be faster and more reliable than saving alone while the group dynamic can encourage reliable participation and reduce risk.

A Risky Business

There is a fundamental tension at the heart of lending: risk versus service. Lenders exist to increase access to capital for those who need it, but every loan decision must be balanced against the risk of the recipient defaulting. Defaults reduce the lenders’ ability to lend to others in the future, which limits their original purpose. Business loan defaults have been rising at the fastest levels since the 2008 crash, leading to a tightening of credit conditions.

The traditional risk calculation is made on available financial metrics that indicate the ability to service a loan such as credit history, existing savings and business performance data. Even this process is usually a compromise, balancing the lender’s need for information with the amount of time that they, or the customer, can devote to information gathering.

Recent research shows that 38 per cent of UK businesses have chosen to abandon a banking services application in the last year due to “slow due diligence processes. Financial APIs have helped to increase the speed and efficiency of this process by pulling in existing financial data, but StepLadder has been able to take this a step further by using APIs to act as financial matchmakers.

The Group that Saves Together, Stays Together

APIs are ideal for collating financial data on existing activity, which helps them expedite traditional due diligence processes. However, there are other factors that indicate a customer’s risk level, such as the context for their credit request, the peer group around them and the consequences of their achieving their goals.

This information is harder to gather automatically due to its non-formatted nature, so StepLadder uses APIs to match their own communities within the platform itself.

StepLadder uses contextual and financial data (from customer input and APIs) to match members with similar goals and needs, creating bespoke communities. Not only does this build in social obligation, it also creates a support network to keep members on track. According to research from National Bureau of Economic Research cited by StepLadder, savings in groups increases the chance of success by 300%. In a StepLadder Circle, 87% of members are in a position to reach their financial goals sooner.

Succeeding Together

The key learning for developers is that this doesn’t require any new data. By using existing risk and KYC data, you can reduce risk further by making connections between similarly profiled users. The same concept can also apply to other forms of software risk, such as churn or poor user experience. By connecting users to help each other, they can build stronger bonds through your app, increasing stickiness, retention and satisfaction based on common profiles and goals.

About the author

Ryan Clifford is the Developer Relations Lead for FusionFabric.cloud, Finastra’s open development platform solution. He is passionate about building the fintech and developer community and helping the ecosystem to connect into FusionFabric.cloud.

Why Communities Naturally Reduce Risk was originally published in Finastra Fintechs & Devs on Medium, where people are continuing the conversation by highlighting and responding to this story.