Innovation Of No Code AI In US Banking & Its Impact On Customer Experience

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Signzy

9 min readFeb 25, 2021

No-Code AI in US banking and how it has impacted customer experience

AI-powered machines are tailoring recommendations of digital content to individual tastes and preferences, designing clothing lines for fashion retailers, and even beginning to surpass experienced doctors in detecting signs of cancer. For global banking, McKinsey estimates that AI technologies could potentially deliver up to $1 trillion of additional value each year.2

Many banks, however, have struggled to move from experimentation around select use cases to scaling AI technologies across the organization. Reasons include the lack of a clear strategy for AI, an inflexible and investment-starved technology core, fragmented data assets, and outmoded operating models that hamper collaboration between business and technology teams. What is more, several trends in digital engagement have accelerated during the COVID-19 pandemic, and big-tech companies are looking to enter financial services as the next adjacency. To compete successfully and thrive, incumbent banks must become “AI-first” institutions, adopting AI technologies as the foundation for new value propositions and distinctive customer experiences.

Over several decades, banks have continually adapted the latest technology innovations to redefine how customers interact with them. Banks introduced ATMs in the 1960s and electronic, card-based payments in the ’70s. The 2000s saw broad adoption of 24/7 online banking, followed by the spread of mobile-based “banking on the go” in the 2010s.

Few would disagree that we’re now in the AI-powered digital age, facilitated by falling costs for data storage and processing, increasing access and connectivity for all, and rapid advances in AI technologies. These technologies can lead to higher automation and, when deployed after controlling for risks, can often improve upon human decision-making in terms of both speed and accuracy. The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually

No-code in a nutshell

For as long as there have been computers to program, there have been attempts to make programming easier, faster, less technical, and available to a much broader audience. Essentially, any end-user programming signals that even though most computer users lack coding skills, they would welcome the application potential of various tools — as long as the effort to obtain these skills is low.

No-code stands for a family of tools that allow people to build applications and systems without having to program them in a conventional way. Instead, the core functionality is accessible through visual interfaces and guided user actions, as well as pre-built integrations with other tools to exchange information as needed.

While these self-imposed restrictions can lead to issues for very large or complex applications, the whole family of no-code tools is handing a big chunk of power to their users. As Alex Nichols from Alphabet’s growth fund CapitalG said:

“No code is empowering business users to take over functionality previously owned by technical users by abstracting complexity and centering around a visual workflow. This profound generational shift has the power to touch every software market and every user across the enterprise.”

To give you a few examples, here are some common things that can be built entirely with said no-code tools (check out Nocodelist for more examples):

Websites and landing pages with Webflow (ours is built with it!)
Web or mobile applications with Bubble, Adalo, Mendix or Thunkable
Chatbots or virtual assistants through Octane AI, Kore.ai, Landbot or Mindsay
Databases through Airtable
Connecting your tool stack with Zapier, tray.io, Integromat, Parabola, or Paragon
E-commerce through Shopify or Weebly
Manage memberships with Memberstack

It is fair to believe that the no-code space is here to stay. AI tools built on these principles are showing that the field not only grows in width but also depth when it comes to the job to be done and technology in place.

Before we move to no-code AI, we will quickly touch on one fundamental question first: When does it even make sense to use AI?

When to use — Why No Code AI Can Help Succeed?

Note that AI can be used for a variety of applications but we intentionally limit our discussion to business applications.

Broadly speaking, AI is particularly helpful when there is some sort of intelligent judgment to be made by humans and when there are many of these on an ongoing basis. We often use the phrase “AI starts where rule-based automation ends” — which makes sense from our viewpoint but should not be generalized (there are tools that go beyond pure automation, e.g. Obviously AI for analyzing tabular data at scale).

Quantity

Due to ever-changing regulations at both the federal and state levels, insurers and banks are finding themselves having to produce, reproduce, and edit a massive amount of required client forms. Whether it’s a policy proposal, an insurance application, a policy amendment, or a prospectus, it’s something that must be reviewed by an employee and scanned for future retrieval. When you use a digital solution like EasySend, your company can significantly reduce the number of forms and make it easy to fill them out and store them. If there is an update to be made, an existing form can be changed and saved in the cloud-based database. The catch is that your client database must meet current federal and state security standards to protect consumer information from data breaches.

Quality

Let’s face it. Many insurers and banks in the U.S. have slowly developed into large and complex enterprises. They are slow to complete their digital transformation because they are often crippled by legacy systems and inefficient processes. The result is that customers don’t enjoy their experiences. With EasySend’s no-code, plug-and-play solution, any insurer can become more adept at processing multiple client forms, which improves the customer experience and elevates overalls satisfaction to a new level. Your insurance company must invest in a solution that brings legacy systems into the digital age (even if it means replacing them).

Cost

We live in a world where the potential ways you could invest in digital transformation would exceed your IT budget if you purchased them all. Every time you adopt a digital solution, ten more options emerge on the market promising to perform the same processes and more with greater efficiency. Although many large insurers and banks have deep pockets, their spending is under constant scrutiny from regulators, distributors, and customers. Generally, the priority of the insurer is always ensuring that you have healthy cap reserves, general account surplus, product embedded value, policyholder or contract owner dividend, etc. EasySend can help enterprises improve their top-line value (earnings) by reducing the substantial direct and indirect costs associated with manual form production and form management. We’ve also planned for how to manage the many risks associated with manual document processing including errors, non-compliance, and client attrition.

Time to Market

It used to be feasible to wait 8 to 12 months for the release cycle of a new digital product. Now, if you were to wait that long, your customers would abandon your brand. Today’s insurers should consider solutions that deliver new digital experiences to their clientele with greater speed. EasySend uses advanced AI (artificial intelligence) and no-code application development capabilities to reduce development time from months to days. Our solution also reduces maintenance costs and simplifies operations. You won’t need any programmers to update business processes with EasySend, but your CTO will find it easy to implement this platform across your organization. Choosing EasySend would be a crucial and impactful step in your digital transformation.

Benefits In Banking — What No-Code AI Helps You Achieve

Shadow IT solutions being built by businesses to resolve immediate needs are increasing the operational risk considerably. According to Gartner, at large enterprises, citizen developers are likely to be four times the number of IT professionals by 2023.

Customer experience transformation is held back by digital skill shortfalls in the workforce. Almost 80% of banking CEOs in a PwC survey saw this as a key challenge to digital transformation.

Dynamic market and regulation landscapes need adaptability at speed, but technology investment is slow in traditional banks. According to a recent Oliver Wyman study, traditional banks take three to six months to launch a new feature, while challenger digital banks do it in just about a couple of weeks.

Legacy systems that don’t integrate well with modern applications, hinder digital transformation efforts, consuming 60–80% of technology budgets for operations and maintenance.

AI and Credit Decisions

Artificial Intelligence provides a faster, more accurate assessment of a potential borrower, at less cost, and accounts for a wider variety of factors, which leads to a better-informed, data-backed decision. Credit scoring provided by AI is based on more complex and sophisticated rules compared to those used in traditional credit scoring systems. It helps lenders distinguish between high default risk applicants and those who are credit-worthy but lack an extensive credit history.

Objectivity is another benefit of the AI-powered mechanism. Unlike a human being, a machine is not likely to be biased.

Digital banks and loan-issuing apps use machine learning algorithms to use alternative data (e.g., smartphone data) to evaluate loan eligibility and provide personalized options.

Automobile lending companies in the U.S. have reported success with AI for their needs as well. For example, this report shows that bringing AI onboard cut losses by 23% annually.

AI and Risk Management

It’s difficult to overestimate the impact of AI in financial services when it comes to risk management. Enormous processing power allows vast amounts of data to be handled in a short time, and cognitive computing helps to manage both structured and unstructured data, a task that would take far too much time for a human to do. Algorithms analyze the history of risk cases and identify early signs of potential future issues.

Artificial intelligence in finance is a powerful ally when it comes to analyzing real-time activities in any given market or environment; the accurate predictions and detailed forecasts it provides are based on multiple variables and vital to business planning.

A US leasing company, Crest Financial, employed artificial intelligence on the Amazon Web Services platform and immediately saw a significant improvement in risk analysis, without the deployment delays associated with traditional data science methods.

At the same time, explicit programming often leads to problems when there are simply too many rules or exceptions to be considered. In that case, AI often works better. For example, it is certainly possible to set up rule-based automation for processing text by using a long chain of words and phrases but in many situations, this wouldn’t be efficient due to high costs or poor performance.

How small banks can make the most of AI?

In several of our conversations with executives of smaller banks like Community banks in the US, it became very apparent that they were seeking a differentiator in their intense competition with the larger banks. Big banks are using cutting-edge artificial intelligence techniques by using in-house teams of Data Scientists and Quants for risk assessment, financial analysis, portfolio management, credit approval process, KYC & anti-money laundering systems. On the other hand, small banks can use AI for achieving operational efficiency and better customer interactions.

Some of the several applications of AI that smaller banks can benefit from are:

Better Customer interaction using chatbots

Accurate recommendations using Recommendation engines

Fraud detection using machine learning algorithms

Conclusion

Digital transformation has erupted at a rapid pace especially with the pandemic crisis making it difficult to execute daily operations on a physical basis. Rapid transformation of banking operations in AI is no joke, and hence with no-code AI one could say that the process can certainly move along faster. While in its infancy no-code AI still leaves a lot of room for skepticism, one can certainly agree to it that this is the way to banking — now and in the future!

About Signzy

Signzy is an AI-powered RPA platform for financial services. No matter how complex your workflow or operational complexity, Signzy is able to completely automate your back-operations decision-making process into a real-time API. This is possible due to a combination of Nebula — Our no-code AI model builder and our Fintech API Marketplace of over 200+ APIs. Today we work with over 90+ FIs globally including the 4 largest banks in India and a Top 3 acquiring Bank in the US. Globally we have a strong partnership with MasterCard and offices in New York and Dubai to serve our customers in the 2 geographies. Our Product team of 120+ people is building a global AI product out of Bangalore.

Visit www.signzy.com for more information about us.

You can reach out to our team at reachout@signzy.com