AI What Practical Benefits It Deliver to Banking Industry

Table of Contents 

How AI-based Credit Scoring is Different from Traditional Models

Advantages of integrating AI into credit scoring

CompassWay solution

The Future of AI in Financial Institutions

The financial sector requires financial institutions to remain efficient and competitive in a constantly changing environment of technological advances, evolving compliance requirements, new risks, as well as more sophisticated fraud

Artificial intelligence ( AI ) is heralding a paradigm shift in how banks and credit unions serve their customers with simple access to credit and revolutionising the industry with the newest technology. Both the banks and customers can benefit from adoption of AI in credit scoring: banks increase the number of customers and improve assessment of risks, while the underbanked customers get wider access to better lending products. 

By using AI, banks can quickly identify credit profiles of customers applying for a loan and approve  applications without human intervention, reducing waiting time and costs. Plus, it helps lenders understand their loan applicants better while reducing the amount of paperwork to be submitted and processed together with an application. Essentially, AI provides banks with a more efficient way to lend money while saving time and money for both banks and their customers.

AI-powered algorithms can be much more specific than humans when calculating a customer's creditworthiness. This allows banks to be very precise at pricing the risk and improving the profitability of a portfolio as a whole. In addition, AI helps lenders analyze inconsistencies when approving or declining a customer's loan application. This reduces the amount of human error involved in making such decisions. 

There are four main lending strategies that AI-powered lending can help banks with. The first strategy is strategic credit scoring: applying mathematical models in analyzing large volumes of data to determine customers' creditworthiness and correct interest rate given the amount of risk. The second strategy is smart repayment scheduling determining customers' repayment terms including duration  prior to granting them a loan. Third is AI underwriting: assessing customers' financial health and business ability before granting them a loan. Finally, smart collection management: helps banks recover lost or unpaid debts through AI-supported direct contact with creditors or debt collectors.

How AI-based Credit Scoring is Different from Traditional Models

Traditional scoring model

Credit scoring models at most financial institutions continue to use the scorecard approach, i.e., utilising factors that were significant at the time of development of such scorecards. And some credit risk assessment models were developed in the 60s! Needless to say, a lot of risk factors have changed since then.

The process of changing key risk factors over time is known as population drift. This drift could be related to changed economic conditions or new credit approaches (new target audience, new credit products, etc.). Updating the traditional credit scoring models is a tedious and time-consuming process, where financial institutions are always trying to adapt to the past. 

Important characteristic of this system is that to be assessed as "scorable," a potential borrower must already have sufficient historical data on previous borrowing behaviour.

In cases where this type of historical information isn’t available (which is a reality  for a significant number of new customers of the banking sector), even creditworthy borrowers are denied access to credit.  

Finally, traditional scoring models fail to account for alternative data such as the ability to incorporate analysis of social media behaviour or digital footprints which are important factors in predicting credit risk.

To increase financial institutions’ capacity to make loans, advanced credit scoring systems are clearly needed.

This is where artificial intelligence (AI) comes in and how AI could make your credit score obsolete.

New scoring model 

New credit scoring models used by fintech lenders differ from traditional models in two key aspects. 

First, is that technology allows financial intermediaries to collect and use a larger quantity of information. Fintech credit platforms may use alternative data sources including insights gained from behavioural traits and smartphone habits, employment opportunities, potential ability to earn, social media activity and users’ digital footprints to build models of creditworthiness for consumers in emerging markets, where standard credit reporting barely exists. In the case of large technology companies (big techs) with existing platforms, data collection extends to placed and received orders data, transactions history, and customer reviews.

The second difference is the adoption of machine learning techniques. In contrast to traditional linear models such as the logit model, machine learning can develop a non-linear complex model using information from all available variables. 

To do so, advanced classification algorithms use a variety of explanatory variables (for example, demographical data, income, savings, past credit history, transaction history at the same institution, and many more) to arrive at the final score which determines whether the person is creditworthy enough to receive the loan and at which rate.

Advantages of integrating AI into credit scoring

Greater speed

Specifically in credit scoring, AI applications are getting increasingly popular due to their ability to speed up lending decisions without any quality or precision compromises. Traditionally, banks applied decision trees, regression, and complicated arithmetical analyses to generate the client's credit score. Today, masses of superfluous, unstructured and partially structured data can be included in the analysis (e.g., social media use, mobile phone activities, etc.) to make smarter credit-related decisions, but with the help of AI, the speed of data processing remains high

Getting one's first-ever credit 

With the help of data science, credit scoring based on AI financial projections regarding the client's income potential and employment opportunities has become more future-oriented in contrast to old-school past-oriented approaches. In this way, more borrowers can get access to credit today (e.g., students, founders of promising businesses, and foreign residents) to grow their businesses and helps them jumpstart their ideas. 

Getting credit for unbanked

Around the world, 1.5 billion people do not have access to the services of banks or financial institutions. These people are referred to as "unbanked." 

Many financial institutions are also reluctant to offer loans to consumers with a damaged or insufficient credit history, believing that the risk is too high.

The risk these customers pose to the institution outweighs the benefits of serving them. This mindset has the potential to be harmful to both consumers, who may mistakenly suffer harm when forced to rely on low-quality credit products, and providers, who miss out on real profit opportunities as well as the opportunity to develop loyal and long-term customer relationships. 

Smarter credit scoring methods based on wealth alternative data are changing that by extending the ability of financial institutions to grant loans to these segments of customers.

Decrease human error

Decision makers during the process of decision-making may not pay enough attention to some important factors, whether it is obvious or hidden. Ignoring these factors could cause making a decision with unwanted consequences. 

Human error is an obstacle that every financial institution faces. Researchers at the Massachusetts Institute of Technology (MIT) Sloan School of Management, Harvard Business School and the University of Southern California published a report  examining the mistakes loan officers sometimes make when deciding which borrowers should receive a loan. One of the most common reasons for loan officers making bad loan decisions? Officers who were distracted before a weekend or near a holiday, which sometimes led to a rushed loan decision. The borrowers could either be given a bigger loan than they could afford or even be declined.

 Profit maximisation 

Business-wise, that means that instead of simply minimising credit losses or maximising sales, a bank should concentrate on profit maximisation.

The use of AI tools for credit scoring and lending decisions can not only increase the number of customers for the bank while decreasing the portfolio risk but also helps banks make data-driven decisions, focus on margin maximisation instead of risk minimization, analyse risk vs. profit curve instead of relying on pre-calculated scoring cards brackets. 

This is an approach that was virtually impossible before the widespread implementation of AI and data-gathering techniques. 

Increase competitive advantage.

FIs are always in competition to provide the best and most efficient services within their industry. Fintechs are setting new standards with the use of technology to provide a higher-calibre customer experience.  To stay competitive, FIs need to also assure their customers that they protect their

data and money using a superior level of security. AI cybersecurity products that mitigate risk, fraud, and compliance which are built specifically for banking provide streamlined and industry-specific protection, catering to your FI’s unique needs.

CompassWay solution

With an intuitive user interface and a proprietary AI-powered Decision Engine, you can get benefits of utilising AI in lending, which will lead to both reduction of credit risks and increased potential growth. Credit decisions, automatic or semi-automatic, if made with CompassWay, take seconds, and ensure you’re working with the right borrowers on the right terms. Deep neural networks are used by the lending platform in its credit decisioning to lower the probability of non-repayment. CompassWay AI solution goes through the whole loan lifecycle – from origination to closure in order to estimate an expected revenue stream.

Based on this, it provides knowledge-based recommendations to enhance your profitability.

The Future of AI in Financial Institutions

According to Mordor Intelligence report, the global AI fintech market is on a steady growth spurt since 2019 with USD 6.67B and is projected to hit a whopping USD 22.6B by 2025.

When financial institutions first started using AI, it was to perform relatively simple tasks. Currently, AI is being used to create a more personalised customer experience, detect and prevent fraud, and optimise back-office operations, and in the future, AI is likely to be used for financial planning and investment decisions. Artificial intelligence can be used to provide personalised financial advice, help banks identify new sources of income and even make lending decisions.

As AI continues to evolve, it is likely to have a profound impact on the banking and financial industry. Those who embrace this revolution will be well positioned to take advantage of this transformative technology.

In summary, the future of artificial intelligence in banking and finance is full of potential, but full of uncertainty. But despite the challenges, it's clear that AI will play an increasingly important role in the industry, changing the way banking and finances are managed. With this in mind, it is vital that we stay ahead of the curve and continue to learn about the latest developments in AI so that we can make the most of its potential.


Valentina Zhukovska Compassway 

 Financial sector professional, combining the knowledge of large-scale bank operations and cutting-edge fintech technology.  Main professional  areas are the transformation of the traditional banking operations into the digital bank and extending this access to banking services to unbanked and underserved clients.

Anna Breus  CompassWay

 Proficient software QA Engineer ,with a creative approach to verify and validate development fintech products that bring real value for Company ‘s  clients .