A Driver of Change – How AI is transforming Risk Analysis in Banking
By Célia Ferreira, Risk Framework-Porto, & Joel Benaroch, Global Head of Market & Counterparty Risk
In a world driven by data, AI models have emerged as key pillars boosting the efficiency of processes and supporting decision-making process. AI is also changing the way the banking industry works. With huge amounts of data generated in real time, AI is capable of analysing, classifying and predicting patterns in financial data. AI is also being used to improve the customer experience and automate processes, reducing costs and increasing efficiency.
Efficient assessment of data quality is critical
To accurately assess risk and support the definition of risk management strategies, risk managers and analysts rely on data-oriented processes but are challenged to detect weak signals in massive heterogenous data sets. Indeed, stress tests and some risk metrics are extremely sensitive to outliers, and so data quality issues deeply influence accuracy.
Stress tests and certain risk metrics are extremely sensitive to outliers, and so data quality deeply influences accuracy
- Célia Ferreira, Risk Framework-Porto
The existence of false alerts is also a source of inefficiency.
To assist with this, AI models can process large volumes of data with high precision and capture complex patterns in data, outperforming other conventional techniques used to identify data quality anomalies. Several processes to check data quality at Natixis CIB have been improved using AI and are now more efficient than ever, minimizing the number of false alerts and anticipating the detection of incorrect market data, which saves valuable time for analysts.
Market moves report is being powered by Generative AI at Natixis
Nowadays, the generative AI models that have emerged are not only able to understand but also create, after learning from large data sets. One of the most significant applications of generative models in risk management is the production of financial and business reports. These reports can be created automatically using advanced AI algorithms to analyse large amounts of financial data, delivering reports with accurate, useful, and necessary information for making business decisions. This can help banks to have concise risk management reports simplifying complex risk data, save valuable time and resources, as well as reducing the risk of human error.
Advanced AI algorithms can analyse large amount of financial data to generate reports with accurate, useful and necessary information for making business decisions
- Joel Benaroch, Global Head of Market & Counterparty Risk
At Natixis CIB, we are using Generative AI to power reports that select and summarize impactful market news. The selection of automatically generated comments provides the most impactful changes observed in some market indicators. Additionally, news sentiment is monitored and displayed, enabling a straightforward view of the market sentiment.
Boosting risk profile analysis
Analytical reviews performed by risk analysts may also benefit from Generative AI. These reviews are in place to deliver a full decomposition of positions to confirm the sources of trading revenues across thousands of risk factors across asset classes. This decomposition often concludes with comments that qualitatively explain the main drivers. Generative AI has the potential to deliver a first draft of such qualitative comments, which would in turn be reviewed by analysts.
The applications of AI in risk management are numerous and offer multiple opportunities to enhance risk analysis, improve efficiency and reduce costs. As AI continues to evolve, we are likely to see the wider adoption of generative models in banks around the world.