Effective risk management is crucial for lenders as it helps them identify, evaluate and mitigate risks. The rapid growth of credit operations, rising customer expectations for personalised solutions and lower turnaround further emphasise the need for robust risk assessment and management tools.
As per a report in Hindu Business Line, retail loans grew at a compounded rate of 24.8% for the year ending March 2023. This is almost double the growth rate of advances for the year ending March 2022.
In this scenario, automated lending systems help lenders meet customer expectations, offer them personalised solutions and manage the rapidly increasing loan applications more effectively. Furthermore, these tools serve as a valuable asset for risk management and eliminate the problems associated with manual risk assessment.
The blog post focuses on 10 ways automated lending systems help lenders mitigate risk.
10 Ways Automation Mitigates Risk for Lenders
Traditionally, lenders carried out credit appraisal and risk assessments manually. This involves extensive paperwork and dependence on obsolete credit scoring models which rely on subjective rather than objective, data-based inputs. These risk management models often lead to inefficiencies, higher turnaround time, errors in judgement, and may also be biased.
Modern risk assessment tools that leverage Artificial Intelligence (AI) and Machine Learning( ML) do away with the problems associated with manual risk management and offer several advantages.
Here’s how lenders can gain in the following ways when they use an automated lending system:
1. Harness the power of data analytics
Automated credit assessment systems can process large volumes of data much faster with better efficiency. Robust data analytics can spot patterns and anomalies that are not apparent to humans, resulting in better risk assessment and decision-making.
Intelligent solutions like Finezza’s Analytics suite provide results that improve and get richer and deeper with each usage.
2. Conduct more stringent credit checks
An automated lending system helps lenders scrutinise loan applications more thoroughly. Financial Institutions (FIs) constantly have to walk the fine line between enhancing their customer base and maintaining the quality of their portfolio. They do not want to deny credit to deserving applications and want to promote financial inclusion; at the same time, they want to minimise the default risk.
Filtering customers at the application stage ensures only qualified customers get loans. Loan automation systems evaluate more data points and sources, usually overlooked in manual processing to assess applicants. AI and ML-based risk scores for borrowers help identify potential default risks.
3. Real-time monitoring
Simultaneous tracking goes a long way in mitigating default risk. Credit health is a dynamic concept, as the creditworthiness of an individual may change with time. If the credit quality of the lender’s portfolio deteriorates due to defaults and missed payments, real-time monitoring can help them take timely action.
With an automated lending system, lending companies can track and monitor loans more effectively and act promptly with responses like sending frequent reminders. Real-time risk monitoring helps reduce bad debt risks with real-time alerts and credit reviews.
4. Eliminate human bias
Besides offering a more accurate evaluation of applicants’ repayment and risk capacity, advanced algorithms of automated lending systems help remove human biases from the process.
Automation of risk assessment guarantees uniform and unbiased evaluation for all applicants. This ensures that only worthy applicants get loans, and no deserving applicants are denied credit due to inherent biases.
5. Reduce Errors
Copious amounts of paperwork involved when processing a loan application in conjugation with the pressure of reducing the turnaround time, can result in errors.
Automation eliminates the risk of manual errors occurring while entering data and processing, which could lead to faulty decisions.
6. Utilise alternative data
Automation also helps FIs look at alternative data sources that traditional risk assessment tools and models usually overlook. Cross-product data from internal sources, social media inputs, online buying patterns, utility bill payment details, and more help provide a comprehensive customer profile and aid risk assessment.
7. Better compliance
The lending sector is heavily regulated; adhering to various compliance guidelines set by regulatory bodies is crucial for all FIs. Non-compliance with these guidelines can result in penalties and make the financial institutions susceptible to instability and fraud.
Automated lending systems ensure timely and accurate complaince. They also help lenders align their policies to the regulatory framework.
8. Predictive analytics
Predictive analytics helps FIs identify potential risks which are likely to occur. This analysis can help lenders take preventive measures and make more informed decisions.
The predictive analysis forecasts future outcomes considering current and emerging market trends and identifies anomalies that may go unnoticed by traditional methods.
9. Minimise Strategic Risk
Automation helps FIs align their underwriting decisions with their risk policy seamlessly. Any updation in the risk policy demands a change in the underwriting process too. Automated loan processing systems require a few tweaks to update the approval process.
Compare this to updating the manual risk assessment process, which would require extensive training and ensuring the updated information reaches each team member and they comprehend it well. This could be a time-consuming, expensive process, fraught with teething troubles.
10. Facilitates risk-based pricing
Automated lending systems facilitate pricing loans in line with the borrowers’s risk profile and ensure that lenders get compensated for the risk they are taking on.
Risk-based pricing allows lenders to offer different consumers different interest rates or variations in other terms, depending on the risk profile perceived by the system.
Risk-based pricing models also enable better risk management. For example, a customer who offers collateral could get loans at lower rates, just like a customer with a high credit score.
Conclusion
Risk is inherent to the lending process; automated lending systems help lenders mitigate these risks and enhance efficiency and customer satisfaction. Financial institutions can perform better credit checks and offer customers risk-based pricing by automating their lending process. As a result, automation helps lenders make intelligent decisions based on data, analytics, assessing alternative data points and predictive analysis, which reduces their risk.
If you are looking for a solution that offers all this and more, then Finezza has the right solution for you. Their automated loan process system is a cloud-based, AI-enabled solution that automates the entire loan processing workflow.
Finezza also offers an all-in-one credit evaluation solution that merges data reports from multiple sources with AI-powered predictive analysis to help lenders make risk-free decisions.
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