AI in Loan Management and Credit Risk Assessment

Loans and credit are some of the main sources of income for banks and financial institutions alike. However, to be profitable with these streams of income, banks need to be precise with their predictions, hence, their reliance on data.

AI systems enable banks to efficiently manage their entire loan and credit assessment lifecycle to be as profitable as they can be. It helps lenders generate accurate predictions from data in a fraction of the effort, allowing them to manage loans and assess credit risk better. This article covers how lenders can leverage AI systems to transform loan management and credit risk assessment to remain profitable.

Why Lenders are Moving Away from Traditional Loan Management and Risk Assessment

Traditional loan management and risk assessment rely heavily on a decent combination of human expertise and financial metrics. Lenders are required to develop relationships with their borrowers, understanding their financial situation and goals. They have to verify the borrower’s creditworthiness by looking at their financial rations (for example. debt-to-income), credit bureau reports, etc.

When done manually, this is a meticulous and time-consuming process. In addition, it is prone to errors, which could lead lenders to incur bad debts [2]. This is more pronounced in corporate banks that serve enterprises and large corporations.

Financial services are increasingly becoming digitized. Loan and credit applicants can now onboard via digital platforms and have their entire lifecycle as loanees managed digitally. More lenders are adopting digital methods to originate and process loans and credit. Studies show that this is happening steadily at a compound annual growth rate (CAGR) of 12% [1].

The challenge posed by digitization in loan management and credit risk assessment

While digitization has brought numerous benefits, it has also come with a set of new sophisticated challenges for lenders. They are presented with a vast pool of data, without any reliable way to efficiently process it. This has made loan and credit underwriting increasingly complex and time-consuming.

Lenders find themselves in a difficult position where they have to invest heavily in data analysis to adequately protect themselves from financial loss. However, when they take too long to do so, they risk losing customers.

In other words, they have to understand their borrowers, situations, and financial goals better, and in a shorter period. This is where AI systems come in.

How AI can Improve Loan Management and Credit Risk Assessment

AI is not new to finance, it has been used to derive insights from data analytics, measure performance, make real-time calculations, deliver customer service through chatbots, and so on. With lending, it holds so much potential to improve it in the following ways.

Accurate credit scoring

Credit scoring is extremely important to financial institutions as it helps to mitigate the risk of delinquency, or worse, a default in payment. A credit score is a numerical representation of a borrower’s creditworthiness. It helps lenders to know how risky it is to give a borrower a loan or credit.

Average default rate by country | Source: Statista

Creating a credit score involves analyzing things like [3]:

Payment history – how well an individual or organization has managed their debts in the past.

Credit utilization – how much credit they use compared to what is available.

Length of credit history – how long the borrower has been using credit.

And more.

Calculating the credit score traditionally has its fair share of limitations. First, since it heavily relies on a borrower’s historical financial data, new customers will likely face difficulty securing loans, even when they are creditworthy. Also, a typical credit score does not apply to new credit products.

Eliminating Bias in credit scoring

AI-powered credit scoring makes lenders less short-sighted. Although it is intended to be neutral, some biases can creep into credit scoring. Credit scores don’t consider factors like rent payments or utility bills on time, which can disproportionately affect people who haven’t used traditional credit products.

When conducting credit risk analysis AI models monitor more relevant metrics like current income, the potential to earn, and employment opportunities. It adds more metrics to credit scoring and increases financial inclusion. This way, lenders don’t have to lose out on potential customers.

Fraud risk management

Fraud detection is a huge concern for lenders, especially now that digital onboarding for loan applications is mainstream. Fraudsters have more avenues to exploit and this has led to banks falling victim to approximately 24,000 fraud attacks annually [4].

AI offers banks a comprehensive way to protect themselves against fraud. With AI fraud detection, lenders can protect themselves from fraud risk in the following ways:

Fraud detection – AI systems can analyze vast amounts of loan applicant data in real time and identify unusual patterns that might indicate fraud. These systems take a lot of variables into consideration such as unusual application times or frequencies and applications from suspicious locations.

The huge advantage of AI is that its machine learning algorithms can continuously learn and adapt. As fraudsters develop new tactics, the system can identify and adjust to these evolving threats [5].

Fraud investigations – Suppose lenders fall victim to fraud, AI systems can help them easily carry you fraud investigations. These systems can scour through tons of documents in minutes and generate a list of flagged transactions that can be used to help their case.

Source: Feedzai

Enhanced identity verification (KYC) – AI models can be used to verify applicant documents and identify inconsistencies in IDs and or forged documents.

Automating the loan management lifecycle

AI systems can help speed up and streamline the entire loan management lifecycle, starting with loan approval. The traditional loan approval is time and resource-consuming. It involves collecting and compiling several documents (government-issued IDs, bank statements, salary slips, etc.) and verifying them manually. This could take days to weeks. With AI, this process is much faster, and in a matter of minutes, creditworthy customers will have their applications approved.

Apart from loan approval, AI can also automate repetitive tasks like document processing, loan servicing, and generating reports [5]. This frees up the lender’s time to focus more on complex cases, customer service, or the strategic aspects of their business.

Improved customer management

It is nearly impossible for any business, let alone banks and other financial institutions, to have customer service agents available at the call center 24/7. However, this is somewhat possible with AI. By leveraging AI models trained on different types of data (text, images, video, and audio), lenders can have human-like AI chatbots available at all times to answer basic customer queries. These chatbots can be configured to help customers retrieve information easily and forward complex cases to human customer service agents.

Tailored loan and credit offerings

Lenders can take advantage of AI’s advanced analytics to tailor their offerings for specific individuals or enterprises [7]. These AI systems can analyze large amounts of data from different sources to give lenders a better understanding of what their customers need. Then, they can use this information to pitch loan offers that align with the borrower’s preferences or financial goals.

Personalization solutions for banks Powered by AI | Source: Emerline

For instance, take a business that has agents who travel regularly at specific times of the week. This type of business spends a significant portion of its capital on plane tickets. With the help of AI models, a bank can identify this spending pattern by analyzing its transaction history. Using this information, the bank can recommend credit card cashback or reward offers specifically for plane ticket purchases. This is a highly personalized service that customers are highly likely to take up.

Challenges and Concerns of AI in Loan Management and Credit Risk Assessment

Banks and other financial institutions face the following challenges when implementing AI into their lending workflows.

Data privacy and security

Lending deals with highly sensitive consumer information that is well-regulated. AI algorithms rely on this data to work effectively. Some customers may be wary of giving up this information if they know it will be fed to AI systems. To work with AI systems, lenders must add an extra layer of robust security practices and adhere to data privacy regulations.

Transparency and explainability

AI models, especially complex ones, can be like « black boxes. » It can be difficult to understand how they arrive at a particular decision about a loan application. This lack of transparency can be problematic for regulators and borrowers who want to understand why a loan was denied or why a certain interest rate was offered.

Fortunately, more AI developers are creating more interpretable AI models and providing explanations for decisions to address transparency concerns.

Bias and fairness

A major concern is bias seeping into AI models. If trained on historical data that reflects past biases in lending practices, AI can perpetuate those biases against certain demographics. This can lead to unfair rejections or less favorable loan terms for qualified borrowers.

While AI offers significant advantages in loan management and credit risk assessment, being aware of the challenges and concerns is crucial. By promoting fairness, transparency, and responsible development, AI can be a powerful tool for a more inclusive and efficient lending system.

Conclusion

AI can be a very powerful tool when integrated into the loan management and credit risk assessment workflow. It enables lenders to improve financial inclusion while mitigating the risk of fraud, delinquencies, and defaults. With AI systems, lenders can take full advantage of their data to enrich the entire loan management cycle and manage credit risk better.