Proactive Credit Risk Management: Developing an Early Warning System

As per a report, as of March 31, 2023, 2,623 borrowers were classified as wilful defaulters in India and owed Rs 1,96,049 crore to the banks in India. This highlights the inherent risks lenders face.

Therefore, financial institutions (FIs) need robust credit risk management to minimise risk and boost returns and productivity. An early warning system can be instrumental in helping lenders manage these risks proactively.

But what aspects should lenders consider before adopting a resilient credit risk management approach? How can they develop early warning systems to stay ahead of the curve every time?

In this article, let’s find out.

Why is Proactive Credit Risk Management a Must-Have for Lenders?

Proactive credit risk management involves anticipating potential lending risks and adopting strategies to minimise the risks and impact they may have on lender operations. This ensures lenders have a preventive approach rather than a reactive one.

Let’s look at some of the major benefits of agile credit risk management:

1. Minimises Risk

Effective risk management helps lenders significantly reduce non-payment risk. FIs can do this through sound credit assessment at the time of onboarding. After loan disbursement, actively identifying customers likely to default or delay payments helps lenders make timely interventions.

2. Improves Profitability

Managing credit risk helps lenders mitigate negative returns. FIs can reduce bad debt losses, helping them improve their bottom line and increase profitability.

3. Strengthens Business Resilience

Lenders who adopt a proactive risk management approach are more agile in response to market changes, regulatory shifts, or emerging threats. Identifying and mitigating risks before they become a crisis diminishes the chances of major disruptions to business operations, ensuring continuity.

4. Promotes Competitive Advantage

Proactively managing risks helps lenders stay ahead of competitors and not get caught off-guard by unexpected events.

Key Components of an Effective Early Warning System

In daily operations, lenders may sometimes miss small yet significant signs that signal risk to their operations and profitability. To solve this, a Loan Management System helps lenders streamline the lending process, manage credit risk, and reduce loan default rates.

However, lenders need to put in place early warning systems as part of their current portfolio monitoring activities on an annual, quarterly, daily or event basis. This is to track events that may indicate potential distress and ensure they do not overlook even the smallest distress signal.

Here are some key aspects of developing and deploying early warning systems:

1. Focus on Seamless Integration

To ensure the most effective use of an EWS, lenders must integrate it with their current loan management systems or lending platforms. This ensures a seamless data flow and provides a holistic view of each borrower.

Collaboration amongst various departments like risk management, credit analysis, and IT ensures that FIs have a holistic approach to credit risk monitoring.

2. Identify Diverse Data Sources

Data from multiple reliable sources provides a more accurate and holistic view. Lenders can use financial statements, credit scores, transaction data, and external data sources like credit bureaus, market intelligence, and economic indicators to pick signs of distress.

Moreover, behavioural insights, such as changes in spending habits or borrowing patterns, are also instrumental in detecting potential credit risk.

Apart from diversity, it is also essential to ensure that the system provides real-time inputs to ensure lenders are up-to-date with changing borrowers and market dynamics.

3. Craft a Risk Assessment Framework

Lenders must develop scoring models to help them quantify credit risk based on historical data and predictive analytics. The models should factor qualitative and quantitative aspects and classify borrowers into diverse risk categories.

Critical factors are quantitative or qualitative benchmarks indicative of current or expected loan performance. These can help lenders monitor the credit quality, solvency, or market risk of the borrowers or the lenders.

Identifying critical indicators like falling revenue or increasing debt signals of potential distress is essential. Predictive analysis helps analyse patterns and predict future credit performance.

4. Define Thresholds and Triggers

Thresholds are predefined values or ranges that signal risk level and indicate the need for action. The criteria for establishing thresholds can be based on historical data, regulatory guidelines, or industry benchmarks.

Lenders should establish clear thresholds for risk indicators, which, when breached, must trigger an alert for further action. Automated alerts and notifications are essential because they warn the credit monitoring team when a threshold is exceeded.

5. Incorporate Feedback Loop

Feedback loops monitor the effectiveness of the early warning systems and suggest improvements when required. Feedback loops involve the verification and validation of data used in risk indicators, thresholds, and reports.

Evaluation, review, and revision of the methodologies, the models, and the assumptions used in risk assessment and management help lenders evaluate the relevance of the early warning systems.

They also promote adapting early warning systems to the changing environment and the evolving needs of the borrowers or the lenders. Feedback mechanisms help define risk models and indicators based on actual outcomes.

6. Leverage Technology

Early warning systems that leverage Artificial Intelligence (AI) and Machine Learning (ML) help lenders monitor and identify delinquent loans or elevated credit risk more effectively.

Advanced analytics tools and software help interpret large volumes of data and support predictive modelling for better risk monitoring.

The Bank Statement Analysis tool from Precisa is an analytics tool that can help lenders identify early warning signs. It extracts data from the bank statement, classifies and categorises transactions, analyses the data, and detects fraudulent transactions and other anomalies.

Final Note

Financial institutions can reduce risk, enhance profitability, and establish a resilient brand image through effective credit risk management. Early warning signs are crucial in helping lenders proactively manage risks and take preventive measures instead of reacting.

Each lender may have a different approach to risk management and threshold for risk. Thus, a one-size-fits-all strategy will not work when looking for solutions. Finezza offers a suite of products that can help FIs manage risk and craft an early warning system suited to their unique requirements.

The Bank Statement Analyser fetches the data in real-time and presents actionable insights in visually appealing, intuitive dashboards.
Credit Bureau Data Analyser provides credit-focused grouping and alerts on data points highlighting risks and a DPD analysis for all the products.
Collection Delinquency Management offers rule-based collection case assignments to the field staff.