How Can Fintech and the Banking Industry Make Use of Explainable AI?

by Maricar Morga  |  Guest Contributor  | Digital Marketing, Business

The Banking and fintech industry can benefit from explainable AI as it can help banks adopt and harness solutions needed in any process normally done manually. Photo by Gerd Altmann on Pixabay

One of the main obstacles to AI banking has also been a lack of clarity about how AI judgments are taken. The secret to tackling this challenge is the explainable AI or XAI method. It can describe how a device or program works in clear language. It can also indicate how decisions have been taken. XAI can also answer follow-up questions that can improve the client's financial well-being.

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Artificial intelligence continues to be part of many sectors around the world. AI is progressively being incorporated into the banking and financial technology (fintech) landscape. However, as AI's adoption is on the rise in banking, there are still some hurdles that the industry needs to address. Some critics argued that AI in banking comes at the expense of banks' decentralization from their customers and the lack of 'human contact.'

Explainable AI And The Banking Industry
Transparency is essential for AI to succeed in the banking industry. By communicating how and why decisions are being made, XAI helps customers and businesses understand what they need to do to achieve a different result. That might entail, for example, transforming a rejected mortgage application into acceptance. 

These days, technological innovations have created an impact in many industries. A sample of these are the business msp software you can find in the market today and other growing tools essential for businesses. Technology allows customers to take appropriate action. That is, while opening up new market opportunities for banks and other institutions that have the information they need to sell more appropriate products. 

The ability of XAI to boost customer service in the banking industry is immense. AI has already taken important decisions on loans, asset management, and even criminal risk assessments. 

Ways XAI Can Impact The Fintech And Banking Industry
1. Personalization At Scale
Personalization is one of the keys these days to make sure that you relate to your target audience. It's hard for the banking industry to live up to personalization. It is difficult as decisions are required to appeal to your audience. Issues like the first question on the application process prevent abandonment.  

XAI provides a significant potential for the banking industry to make the process simpler and quicker. In the previous years, designers for the banking and fine-tech industry have had to run A / B tests to see which solution has been most successful for most users, and then develop their app accordingly. With the introduction of AI and machine learning, there is no need to pick only one variation — systems today can anticipate and personalize each individual's experiences. 

2. Helps In The Onboarding Process
Good customer onboarding will make a lot of money available to many banks. If these systems are versatile, banking customers will not miss out on potentially cost-effective loans that cost millions. Banks offer better digital self-service journeys with the help of XAI. It rapidly carries out complex eligibility tests while conforming to bank risk requirements. It can set up acceptable pricing models while retaining customer transparency and retaining risk controls. 

3. Credit Scoring Of Future Clients
Many options for AI applications for credit scores rely on the AI application's ability to provide a comprehensive description of its recommendation. Beyond enforcement, the importance of XAI can be seen for both the customer and the financial institution. Customers can obtain explanations that provide them with the details they need. This info can enhance their credit profile, while providers can better understand the anticipated customer turnover and modify their services. 

For example, the XAI model can provide an overview of why a pool of assets has the best distribution to mitigate a covered bond risk. 

4. Limit Human Biases
Banking is an inherently error-prone operation, depending solely on human decision-making. Natural biases are present whenever value decisions are taken. That is significantly when credit lines are extended. Questions like w ho's going to earn credit or how much are they entitled to receive are raised. 

Early AI applications have shown that there is a genuine danger that software will potentially reinforce human prejudice, especially along the lines of race. Explainable AI enables quality control engineers to identify where these prejudices can be accentuated so that banks can teach their algorithms to XAI products to recognize and remove such cases. 

5. Decline Of Compliance Cost
Financial firms that have AI / ML algorithms can have a production log in a model. It can generate all the appropriate forecasting risk management documents in minutes. That is instead of the compliance team manually taking weeks to do so. Automated tools often reduce the time it takes to perform equal lending tests by creating less biased flying models than a time-consuming drop-on-variable-and-test approach. Time is money, especially in lending businesses.  

6. Automation Of Online Process
Banks and financial institutions are embracing AI technologies to leverage knowledge. It is also because of insights locked into unstructured documents and automating the conventional bank manual process in a double-quick timeframe. In  April 2020, Temenos, a banking software company, announced eight proposals using advanced Explainable AI (XAI) and cloud technology to help banks and financial institutions respond immediately to the Covid-19 crisis. 

Process automation and increase have generally been well received in several industries. That is because of the workers' perceived benefits, including increased efficiency and improved quality of work. It is also eyed to decrease stress and the opportunity to concentrate on more exciting work. 

7. Introduction Of Newer Guidelines
AI solutions, in many cases, including natural language processing ( NLP), cybersecurity, and fraud detection, do not function as expected due to the complexities in the application context. That is also due to the need for an intelligent interpretation of the model results. This performance expectations discrepancy highlights the need for a new class of product and technology management guidelines better suited to Ai systems.

Final Thoughts
Looking at the real value that AI brings to the table, it's not hard to see why its use is becoming common in the worldwide financial industry. It has a way to filter through large quantities of data. It can do this much faster than a human individual and is less subject to mistakes. 

Looking ahead, digital banking platforms and AI technologies will gradually become the primary way customers communicate with banks. However,  if not done correctly, the customer's disintermediation may harm the banking sector. By embracing customer-centric innovations such as XAI, the familiarity of the customer who has played such an essential role in banking history can be preserved and taken to greater levels.

About the author:

Maricar worked as a marketing professional for almost a decade and handled concerts, events and community service-related activities. Leaving her corporate job for good to pursue her dreams, she has now ventured in the path of content writing and currently writes for Softvire Australia - the leading software e-Commerce company in Australia and Softvire New Zealand. A Harry Potter fan, she loves to watch animated series and movies during her spare time.

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