Risk management is becoming an increasingly important aspect of financial services, ensuring a firm’s profitability and competitive edge. The shifting risk landscape in finance calls for faster and more accurate solutions for risk management. With the rise of new rules and regulations, financial service firms need to avoid risks that lead to non-compliance. Additionally, firms must constantly improve risk monitoring of fraud, financial crimes, credit, and trading activity to reduce costs that arise from forecasting errors.
As a result, financial service firms are turning to artificial intelligence (AI) and machine learning (ML) for risk management solutions. According to a 2020 report by the Cambridge Centre for Alternative Finance (CCAF), 56% of 151 financial service firms surveyed use AI for risk management, making it the most common application of AI. Across commercial banking, asset management, and insurance, new advances in AI/ML technologies are being applied to mitigate risk exposure.
Among these AI technologies are ML-powered predictive analytics, which can help financial service companies mitigate risk by using existing data to forecast future outcomes. While predictive analytics has been around for decades, more recent advancements in the speed and ease of machine learning, as well as the increasing availability and volume of data, have led to optimized predictive analytics capabilities for finance. Unlike descriptive analytics that rely on historical data, predictive analytics using ML techniques enables real-time, autonomous insights.
Within commercial banking, advanced predictive analytics can improve the accuracy of fraud detection models. Within asset management, automated intelligence can help predict trader behavior. Insurance companies can protect against default risk using machine learning (ML) algorithms to predict which borrowers are more likely to default on a loan payment. Across financial services, the predictive power of machine learning is being used to improve the detection of financial crimes, meet capital requirements, and build more resilient risk management systems.
5 Predictive Analytics Use Cases in Risk Management for Finance Services
1. Fraud Detection
According to a report by Nilson Report, fraud losses are anticipated to reach 49.32 billion by 2030. An unfortunate side effect of the rising digitization of commercial banking, such as electronic payments, is higher exposure to fraudulent transactions. For this reason, AI technologies such as ML algorithms used to detect credit card fraud in the middle office have become crucial over the last few years. By training ML models on historical payments data, commercial banks are able to predict whether or not a payment transaction appears fraudulent.
In traditional fraud detection models, computers analyze structured data against rule sets that make up the prediction model. However, in this approach, the models are more susceptible to picking up false positives because they are unable to work outside the designated rule sets and identify conditions in which the rules do not apply like humans can.
With advanced ML capabilities, models continuously learn and improve based on the input of larger and larger datasets, improving fraud detection accuracy. The more accurately the predictive model is able to pinpoint fraudulent activities, the more banks save on operational losses from manually reviewing alerted transactions and fraud prevention.
2. Anti-money Laundering (AML) Transaction Monitoring
Much like with fraud detection, the rise of digital banking and pressures to meet financial crime compliance are accelerating the need for more accurate monitoring of money laundering. Anti-money laundering (AML) references how a firm prevents the conversion of illegally obtained money into legitimate money through commercial transactions.
More recently, in an effort to ensure digital assets meet global AML compliance, companies such as Coinbase, Fidelity, and Robinhood, joined to back the U.S.-based anti-money laundering group, Travel Rule Universal Solution Technology (TRUST). In an age of crypto-based assets and digital transactions, there is a need that the technology used to combat financial crime is equipped to match the increasing sophistication of said crimes.
ML is being used to augment traditional approaches to monitor AML through segmentation, alert scoring and hibernation, and scenario replacement. Monitoring thresholds are often set to be too sensitive to suspicious behavior and thus generate too many false positives. Using ML techniques, financial service firms can segment customers into groups and subsequently establish different thresholds based on each customer group to reduce the number of false positives.
Likewise, applying ML algorithms to alert scoring and hibernation enables financial service firms to automate decisions on whether to hibernate or flag an activity. Finally, ML techniques can replace transaction monitoring scenarios, which are limited by the number of parameters they can contain. With ML, financial service organizations can capture more features and thus more accurately forecast financial crimes.
3. Credit Risk Modeling
Credit risk modeling is used by banks and other lenders to evaluate the risks associated with lending to a particular borrower. One type of credit risk is default risk, which refers to the likelihood that a borrower will fail to pay back their loan. With the abundance of structured and unstructured credit risk data available, ML can help finance companies organize and draw meaningful insights from these various data types.
Once the credit risk data has been selected, machine learning techniques such as data imputation, data filtering, and management of outliers are applied to improve the quality of the data. From there, there are several types of ML algorithms that lenders can apply to datasets to uncover credit risk, such as logistic regression, Naïve Bayes, decision trees, and neural networks. Credit scorecards produced by applying logistic regression, Naïve Bayes, decision trees, or neural networks rely on a classification model, which attempts to predict one or more outcomes based on one or more data inputs.
Using ML for credit risk enables financial service firms to uncover subtle and nonlinear relationships, leading to more robust credit risk models. Using these models, firms can capture indicators of known and unknown risks. Identifying these indicators helps banks, insurance companies, and other lenders manage risks associated with unknown events before they take place.
However, while ML applications in the credit risk industry are on the rise, firms should ensure that model techniques are transparent to reduce regulatory concerns and build trust in AI.
4. Trade Surveillance
Changing environments in the market require financial service firms to update their approach to monitoring suspicious trades. For example, firms must manage and oversee the increase in trading volume from new asset classes such as cryptocurrency. The rise of mobile trading and high-frequency trading, a form of algorithmic trading that executes high volume orders based on market conditions, have also contributed to the increased trading volume and complicates trading surveillance for firms.
Implementing AI systems to monitor trading activity enable firms to monitor suspicious activity (typically fraudulent transactions) by feeding the AI models real-time data on trader behavior such as email traffic, calendar items, office building check-in and check-out times, and telephone calls. AI models are then tuned to alert suspicious behavior based on trader activity in real-time. Additionally, using advanced AI analytics, asset managers can extract sentiment from unstructured data such as voice, video, and other electronic trader communication to detect whether an individual trader has a fraudulent intent based on their tone.
5. Stress Testing
In a stress test, plausible risk scenarios are applied to evaluate whether a financial firm or bank would have sufficient capital to sustain itself under the applied scenario. Within firms, it can also be applied to determine how a specific portfolio would respond given a scenario. Many countries oversee stress testing compliance for financial firms and banks. For example, the European Banking Authority oversees an annual EU-wide stress test exercise that includes at least 50 percent of each national banking sector. In the United States, the Federal Reserve Board requires banks with $10 billion or more in assets to evaluate and publicly disclose the results of their capital adequacy through the Dodd-Frank Act stress testing (DFAST). As a consequence of public disclosure requirements, firms and banks are incentivized to improve risk management controls to strengthen their capital position.
Amidst pressure to meet regulatory requirements and strengthen risk management systems, financial analysts are turning to advanced prediction techniques to improve the ease and accuracy of stress tests. Notably, ML for stress testing supports the processing of large amounts of data and feature extraction, which is the extraction of attributes that a company wants to analyze from the data. The results are models that reflect a wider coverage of risk factors and thus demonstrate greater accuracy for firms.
The Next Stage of Advanced Analytics in Finance
What’s next for advanced analytics for financial services? While predictive analytics is still being adopted across the financial service world, firms can augment the predictive power of machine learning by combining it with prescriptive analytics. Prescriptive analytics algorithms translate what the data predicts to how it should be applied to a business process. Over the last decade, it has taken an increasingly center stage in finance and other areas as a method to fully realize the benefits of advanced analytics. In finance, ML can be applied within prescriptive analytics to improve decision-making and optimize trade-offs between business priorities, by answering questions such as how to best meet stress testing standards.
While firms are increasingly recognizing the value-add of ML-driven analytics, there are still missed opportunities to deploy AI/ML technologies to streamline business activities. In a study by Forrestor, advanced analytics or artificial intelligence contributes to 40 percent of workloads, despite 98 percent of IT decision makers recognizing the importance of analytics to drive business priorities. However, financial service firms that apply ML analytics can build greater resiliency into their risk management processes. Implementation can result in a lower cost base for firms due to less need for human intervention and greater efficiency in the analysis process, better quality services for customers, and higher alpha derived from improved accuracy levels.
To learn more about applications of advanced analytics in finance, read our blog post on predictive analytics in insurance or request a demo of the Accern platform today.
