Role of AI in Risk Management

Artificial Intelligence (AI) and Machine Learning (ML) are the two techniques that have proven their effectiveness in almost every field. Financial services are no exception to this. For example, the banking industry is increasingly adopting AI and ML to leverage their powerful capabilities.

There are several things, from chatbots to fraud detection, where the banking industry uses AI and ML. These techniques help automate processes and streamline operations for both the front and back offices, and enhance the overall customer experience.

AI's following uses and benefits help us understand its role in risk management.

Benefits and Uses of AI

By generating large quantities of timely, accurate data, AI and ML solutions allow financial institutions to build competence around customer intelligence and enable the successful implementation of strategies and lower potential losses.

AI and ML-powered risk management solutions are also being used for model risk management (model validation and back-testing) and stress testing since global prudential regulators require it. AI and ML solutions provide the following benefits –

Superior Accuracy in Forecasting

Traditional regression models cannot adequately capture non-linear relationships between a company's financials and the macroeconomy, especially in the case of a stressed scenario. Machine learning provides improved accuracy in forecasting due to models’ ability to capture nonlinear effects between risk factors and scenario variables.

Optimized Process of Variable Selection

Feature/variable extraction processes are significantly time-consuming in the case of risk models used for the purpose of internal decision-making. ML algorithms integrated with Big Data analytics platforms can process huge volumes of data and extract multiple variables. A rich feature set having a wide coverage of risk factors leads to robust and data-driven risk models for stress testing.

Richer Data Segmentation

Appropriate segmentation and granularity are critical for dealing with changing portfolio composition. ML algorithms promote superior segmentation and also consider several attributes of segment data. Combining distance and density-based approaches for clustering using unsupervised ML algorithms becomes a possibility, resulting in greater explanatory power and modeling accuracy.

The following uses of AI-based solutions are useful in risk management.

Credit Risk Modelling

Banks are usually using traditional credit risk models for predicting continuous, categorical, or binary outcome variables (default/non-default) since ML models are not easily verifiable for regulatory purposes and are slightly complex to interpret. But still, they can be used for optimizing parameters and improving the variable selection process in existing regulatory models.

Despite having nonlinear characters, AI-based decision tree techniques can lead to easily traceable and logical decision rules. You can use unsupervised learning techniques to explore the data for traditional credit risk modeling. Whereas classification methods such as support vector machines are able to predict key credit risk characteristics like PD or LGD for loans.

Financial services firms increasingly hire external consultants. Under stress scenarios, these consultants use deep learning methods to develop revenue forecasting models.

Fraud Detection

For years, banks have been using machine learning methodologies for credit card portfolios. They have a rich source of data on credit card transactions on which they can process and train unsupervised learning algorithms. Because of models’ availability to develop, train and validate large volumes of data, these algorithms are historically known to be highly accurate in predicting credit card fraud.

Workflow engines that are embedded in credit card payment systems monitor card transactions to assess the likelihood of fraud. Banks are enabled to differentiate between specific features present in fraudulent and non-fraudulent transactions, with the rich transaction history available for credit card portfolios.

Trader Behavior

Technologies such as text mining and natural language processing are increasingly being used for monitoring trader activity for insider trading, rogue trading, and market manipulation.

These systems can predict the probability of trader misconduct by analyzing check-in/check-out times, calendar-related data, email traffic, and call times combined with trading portfolio data. In this way, they save millions in reputational and market risk for banks and other financial institutions.

After going through the above article, you can easily guess that Artificial Intelligence is a must for every financial institution for risk management. AI and ML techniques help effectively manage financial risks.?

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