Diagram 1. Types of risks in banks.
As a result of multiple examples of credit and non-credit risks in banks, a tool for assessing them and mitigating their impact appeared—a credit risk management system.
Credit risk management is a set of principles, practices, tools, and techniques aimed at improving loan portfolio through mitigation of losses and assessment of risk/profit ratio.
How to reduce credit risk exposure intelligently
The reduction of credit risk is the utmost goal for lenders. But it’s the road of many miles and obstacles without a comprehensive strategy and planning.
The risk environment is changing constantly. Financial crimes become incredibly sophisticated. Shrewd criminals invent new hard-to-detect fraudulent schemes every year.
Legal compliance regulations are also changed accordingly to keep up with new types of financial crimes and effectively fight fraudsters.
Lenders have to keep their eyes peeled to stay compliant and ensure high-security standards in detecting potential risks and portfolio risk management.
Risk management becomes even more complex when lenders offer a greater variety of loan products in their portfolio.
That makes them have an effective risk management system for accurate identification of risk factors specific to each loan type and applicant for careful risk management throughout the loan lifecycle.
In these complex conditions, the best option for credit risk reduction is the implementation of an all-in-one digital platform supporting the management of various types of loans.
Three major areas of credit risk management
The software solution should be capable to cover three major areas of credit risk management to help lending companies mitigate risk effectively:
- Fraudulent scheme analysis. The deployment of up-to-date digital platforms that mine and collect data is more effective and secure than relying on loan officers guessing and assessing an application to identify hidden patterns.
- Customer verification capability. For credit risk mitigation, advanced data-driven customer verification services are vital for helping lenders to differentiate between potential fraudsters and honest borrowers.
To ensure effective risk management, lending software solutions should deliver:
- Synthetic identity recognition option for comparing provided customer data with information from reliable credit databases.
- Income verification capability for extracting information about customer financial status from confidential and secure sources.
- Alternative credit data analysis for providing a lending company with additional information about an applicant for a more accurate risk assessment.
- Employment history verification for accurate income calculation.
- Collateral assessment capability for better credit limits evaluation.
- Machine learning algorithms and process automation. For effective loan portfolio management and fast application processing, financial companies need to implement automated software platforms and ML algorithms. This allows them to streamline routine tasks and automate the processing of early warning default signs.
Credit risk management is a continuous cyclic process including certain continuously repeating stages from potential risk identification to its treatment and reviewing. The typical credit risk management cycle is shown in Diagram 2.
Diagram 2. Credit risk management cycle
Credit risk management techniques. Opportunities for digitization
Know Your Customer (KYC) and customer onboarding
Since KYC is a compulsory process imposed by regulators on banks and financial organizations to ensure money laundering and terrorist financing prevention, it can be turned out to be useful for lenders as well.
Intelligent digital KYC creates a perfect opportunity to build a comprehensive customer profile. Filled with up-to-date data, the profile provides all relevant information necessary for regular screening of politically exposed persons (PEP) and sanction lists, or for regular credit rating updates.
Automated and digitized KYC processes can considerably improve customer onboarding due to automatic searching and collection of customer data.
Creditworthiness evaluation
Creditworthiness assessment is usually based on financial statements and balance sheet analysis. The process is rather time-consuming and slow if performed manually. This significantly slows down credit decisioning speed and increases costs.
The use of solutions based on Artificial Intelligence (AI) ensures automation of the spreading of financial data from banking statements and balance sheets. The information is intelligently captured, processed, and assigned to the certain categories.
By means of Natural Language Processing (NLP), banks and financial service providers can monitor and analyze customer behavior on social media for getting more data about them to identify positive or negative changes in their financial status.
On the basis of that data, financial companies can build better customer profiles and make more accurate credit decisions.
Risk quantification
Risk quantification is based on the determination of the probability of default, risk-adjusted return on capital, and Loss Given Default. That parameters provide lenders with grounds for loan pricing and shaping credit terms.
Modern technologies allow banking organizations to automate these processes instead of relying on loan officers’ decisions, thus minimizing human error occurrence probability.
Machine Learning algorithms and models do not simply calculate the borrower’s ability to repay. They also evaluate other criteria influencing loan pricing, such as customer loyalty, the elasticity of price, etc.
Credit decisioning
The decision-making process has always been a pain point in the banking industry. Sometimes, it took months to make a credit decision.
Now, when queries are becoming more and more personalized, complex, and, consequently, more time-consuming, it’s necessary to shift from manual decisions to automated ones.
Credit approval acceleration caused by automated digital solutions implementation made the decision-making process faster, more efficient, accurate, and therefore more attractive for current and potential customers.
Calculation of price
Nowadays, the “one size fits all” approach, used by banks in the past years, is not the best option for credit term calculation. It narrows the limits and makes creditworthy customers pay higher premiums to cover the expenses of banks on riskier customers.
Machine Learning enables the accurate individual probability of default calculation and reliable customer repayment performance predictions. Lenders and financiers can replace old, outdated pricing schemes with dynamic personalized risk-based pricing.
Personalized risk assessment causes better and more customer-beneficial loan pricing. That consequently increases customers’ trust in a bank and builds strong customer retention in the long run.
Accurate credit limit setting
Accurate setting of credit limits majorly depends on a thorough customer financial status examination. To set the correct credit limits, it’s necessary to scrutinize key indicators of customer financial health.
The process is usually complicated and cumbersome. Due to the implementation of digital technologies, crucial customer data is collected and analyzed many times faster. Specialized solutions score customers fast and effectively, thus minimizing credit risk and setting an accurate credit limit.
Early warning and customer monitoring
It is vital for banks and lending organizations to constantly monitor their borrowers’ current financial situation. That allows them to detect early warning signals of customer default and react to them in a timely manner.
The use of Artificial Intelligence and Machine Learning procedures enables the calculation of the probability of delayed payment so that financiers could respond accordingly by changing customer credit lines, terms of credit, interest rates, etc.
Risk management system. Is it as effective as it could be?
Nowadays, one of the most productive and efficient ways of credit risk reduction is to benefit from a wide range of data sources available to financial companies.
Extracting, collecting, analyzing, and managing customer financial data used to be a cumbersome and time-consuming process. With new advanced data-driven technologies in hand, all these processes can and must be automated.
Up-to-date digital solutions can handle and automate risk management throughout all the steps of the credit risk management framework illustrated in Diagram 3.
Diagram 3. Credit risk management framework.
By leveraging best-in-class lending automation systems, banks and financial organizations can ensure credit risk mitigation at every step of the lending process, from loan application processing to account closing.
Due to the implementation of automated decisioning, fraud analytics, verification services, and Machine Learning algorithms, lenders can protect themselves and their customers from avoidable vulnerabilities and threats.
Modern times—modern tools
It’s impossible to manage the lending business of today by using tools and techniques from yesterday. Modern customers are seeking complex and individual financing coupled with faster decisioning and suitable risk premiums.
Outdated, manually done credit risk management processes should be replaced with digitized ones. Top-notch technologies like AI and ML considerably raise risk assessment efficiency.
Digitization of credit risk management can support banks and other financial entities by automating any aspect of lending from applicant verification and risk evaluation to loan monitoring and non-performing asset management.
