Machine Learning: A Helping Hand for Financial Firms to Implement IFRS 9
The implementation of IFRS 9 has been an enormous challenge for financial organizations. This regulation requires financial firms to re-assess their recognition and measurement of expected credit losses, resulting in a significant impact on reporting and decision making. It is no surprise that financial institutions are seeking innovative solutions to tackle this data-intensive and complex process. One cutting-edge technology that has emerged as a game changer in this regard is Machine Learning.
What is IFRS 9 and What Challenges Come With It?
IFRS 9 replaces the International Accounting Standard (IAS) 39 on financial instruments, the old methodology required financial firms to wait to recognize losses until a loan was in arrears (part of it was overdue). IFRS 9 introduces an “expected loss model,” which requires financial firms to estimate the probability of losses – both upfront and throughout the life of the account based on information surrounding credit risk, the borrower’s financial condition, and other variables.
The standardized approach to calculating expected credit losses under IFRS 9 is significantly more data-intensive than previous practices since organizations must use historical information and forward-looking information alike to apply their models. It requires gathering large amounts of data from several sources, including traditional structured data (customer data) and unstructured data (social media, third-party data, etc.), making the process more time-consuming and complicated.
The Role of Machine Learning in IFRS 9
One of the benefits of Machine Learning is that it can automate IFRS 9 compliance and help organizations adhere to the regulation’s complex data requirements. With large amounts of data to analyze and interpret, Machine Learning can help organizations train models to predict expected credit losses better, pinpoint patterns in customer behavior, and improve risk identification and assessment models.
By automating the entire process of collecting relevant data, analyzing it, and generating reports, Machine Learning helps organizations free up their human workforce’s time and get faster, more consistent, and more accurate results.
Examples of Machine Learning Applied to IFRS 9
Several financial organizations have started to integrate Machine Learning models within IFRS 9 compliance. One example is a collaboration between ZestFinance and HSBC, where Machine Learning was used to develop credit scoring models, taking into account historical data and more complex variables. The model enables the identification of factors influencing credit scores, which are then used to predict expected credit losses.
Another example of Machine Learning applied in IFRS 9 compliance relates to reserving tasks. Machine learning can automate the entire loan provisioning process, from gathering data, evaluating credit risk, to analyzing loans over different stages, enabling organizations to provide more accurate provisions.
Conclusion
With the complex nature of IFRS 9, financial organizations need a more effective and efficient way to comply with the regulation. By leveraging Machine Learning technology, financial firms can automate many of the processes that come with IFRS 9, saving time, reducing errors, and gaining more insightful data. Machine Learning can aid in developing a more accurate and comprehensive approach to credit risk management, reducing the chances of unexpected credit losses, and enhancing their regulatory reporting capabilities. Therefore, Machine Learning is a technology that financial institutions must consider to achieve a more effective IFRS 9 compliance regulation.
