Credit risk

What is credit risk?

Credit risk is a banking and finance term, meaning the financial risk a lender faces under the conditions their repayment failure – loan repayment, credit card bills, or bond payment. In simple terms, credit risk is a potential stage where a borrower can’t or won’t pay the money they owe, causing financial risk to the lender. An example of a credit risk would be a bank issuing a mortgage to a homebuyer who might lose their job later, causing interruption with repayment. 

Credit risk is a crucial factor for banking, finance, and insurance sectors: which involves analyzing the creditworthiness of customers, monitoring their financial performance over years, and setting the right lending limits. 

Credit risk vs liquidity risk 

Credit risk and liquidity risk may sound similar but aren’t same. Credit risk denotes the case where the borrower fails to repay financial obligations; and liquidity risk is where a company/person fails to hold immediate cash or liquid assets to manage short-term needs. Both credit risk and liquidity risk are connected though—a company or a person facing liquidity risk might not be able to make credit repayments—affecting credit risk. 

Factors influencing credit risk

Factors influencing credit risk are the borrower’s credit history, market conditions, financial stability, loan repayment duration, business performance or career growth, and other external factors.   

Credit history: Credit history is a detailed, financial report of all the credit accounts, a person or business has opened and closed; along with borrowed credits, timely and delayed payments, and more. This is a window to a borrower’s repayment history, that decides the creditworthiness of them. 

Financial stability: Stability denotes the borrower’s financial backups and strength: assets, cash flow, liquidity, other income sources and everything that makes a smooth repayment possible. 

Market conditions: Market fluctuations, inflations, economic downturns or upturns could also affect credit risk. These conditions could make it easy or tough even for financially stable borrowers.  

Loan terms: How the repayment plan is structured, interest, and structure are also crucial factors for credit risk.  

Business performance & leadership: This applies to borrowing businesses: any industry related risks, regulatory shifts, poor sales, leadership changes, or weak governance.  

External factors: This includes natural calamities, global events, or any other unexpected turn of events that affect repayment. 

The five C’s of credit

Most lenders use the five Cs of credit to assess the creditworthiness of the borrower—character, capacity, capital, collateral, and conditions. 

Credit risk analysis – manage credit risks

Credit risk analysis is a data-driven credit management process that businesses and financial institutions can use to evaluate borrowers’ creditworthiness. The step-by-step process to credit risk analysis includes the following: 

Data collection: This is about gathering required financial data of borrowers: credit history and scores, income & cash flow statements, balance sheets, and other financial documents & data.  

Build risk models: The second step involves building credit risk scoring models—traditional, statistical, or AI-based risk scoring. Traditional models use scoring frameworks and involves manual analysis to determine credit risk. AI-based models use machine learning to analyze historical patterns in the collected financial data.  

Risk assessments: With the power of data analytics and AI, lenders can predict credit risk, involving both financial data and external factors. For example, there is scenario analysis process that tests and predicts outcomes by analyzing conditions like inflation. Similarly other AI-based use cases like behavior analysis can also be to determine credit risks, categorizing borrowers into low, medium, and high-risk categories. 

Decision and approval: Depending on the model/method used, risk levels are assessed, and credit decision is approved or rejected.  

Proactive monitoring: Even after the credit approval, risk analysis continues—borrowers’ financial conditions are tracked to raise any early warning and make proactive interventions. 

Why AI and data analytics for credit risk analysis?

Credit risk analysis, as a manual process, could be time-consuming and lead to incorrect predictions & financial losses. Using AI and data analysis could be game-changing, leading to better decision-making & calculated risks. Some AI use cases for credit risk scoring & analysis: 

Accurate credit risk scoring models: Traditional scoring models cannot access beyond financial data; AI expands beyond this, looks into hidden patterns, social behavior, spending habits, and external factors. 

Real-time assessment: AI can process a large amount of information and share risk scores in real time. Likewise, it can improve its outcomes over time. 

Document extraction using OCR: AI-based techniques like OCR can be used to extract data from financial statements and documents to assess risks faster. 

Dynamic risk monitoring: AI can be used to keep an eye on risk profiles and update based on changes in new data, helping financial institutions make proactive changes. 

AI can be an accelerator in credit risk analysis and implementing AI-based credit risk management can reduce risks, expedite loan processing, and improve decision making.