Capabilities of AI for Risk Management

Artificial intelligence is ideally suited for risk management applications due to its ability to augment human intelligence, and rapidly process and analyze enormous data sets. Here are five key capabilities that make AI invaluable for financial risk management.

Access to unstructured data sources

One of AI’s flagship benefits is its ability to process and analyze unstructured data. Unstructured data represents an underutilized resource in risk management. This data can include text documents such as analyst reports, loan applications, customer emails, news articles, and geospatial data including satellite imagery for analyzing exposure to natural hazards such as flooding and landslides. Specialized AI-driven technologies such as natural language processing (NLP) and deep learning are required to derive insights from unstructured data.

Improved forecasting accuracy

AI can rapidly process vast, complex data sets to uncover trends and patterns that human analysts would be unlikely to spot. AI-enabled forecasting models have several advantages over traditional models. AI models produce more accurate forecasts due, in part, to their ability to detect complex, nonlinear relationships between scenario variables and risk factors. AI models can also automatically aggregate data from internal systems as well as historical and third-party sources, incorporating new data as it is created to ensure models are continuously updated.

False positive reduction

Financial institutions continuously monitor for a range of illegal activity — including money laundering; adverse media screening; sanctions screening; and credit card, wire and check fraud. With highly tuned parameters, AI models can reduce false positives, helping financial institutions apply their resources more effectively. In addition to delivering superior accuracy, AI-powered monitoring tools can pinpoint the information most relevant to an investigation, allowing human investigators to identify and address material risks more efficiently.

Faster risk detection

Applying AI to risk management activities such as fraud detection and the analysis of market risks can help financial institutions detect and respond to risk more quickly. One example is AI’s use of parallel processing. Advanced models such as neural networks leverage this computational method to split up data and computations across multiple processors in parallel, allowing them to process and analyze enormous amounts of data more quickly than traditional methods.

Continuous improvement over time

Unlike traditional, rules-based systems that operate based on a rigid set of prewritten rules, AI models can learn and adapt over time. As they are exposed to more examples, AI systems automatically fine-tune their parameters and knowledge to become more effective the longer they are in use.

Using AI in Risk Management

Organizations throughout the financial services industry are incorporating AI into their risk management practices. AI-powered solutions can enhance the effectiveness of existing risk management frameworks and pave the way for more dynamic, data-driven risk mitigation strategies.

Identifying market risks

With its ability to process diverse data at scale, AI is well suited to identifying market risks. AI technology can be deployed as an early warning system, continuously monitoring financial markets, news sources, weather forecasts and other data streams to identify many market risks, including economic downturns, political instability, natural disasters, regulatory changes and newly introduced legislation that could impact markets.

NLP models are one example. These tools can analyze enormous quantities of textual data — such as news articles, earnings reports and social media posts — and use them to quantify the prevailing sentiment and outlook of market participants. This can help financial institutions adjust quickly to changes in investor sentiment or public perception.

Credit risk modeling

Credit risk modeling is another application of AI in risk management. Credit risk modeling helps financial institutions determine the level of credit risk incurred by extending credit to individuals or businesses. AI helps financial institutions proactively manage credit risks, resulting in more-informed lending decisions and better-managed credit portfolios, which ultimately reduce loss and boost profitability. AI models continuously monitor customer portfolios, providing credit analysts with real-time alerts related to bankruptcy, negative changes in credit scores and shifts in payment behavior.

Anti-money laundering

Anti-money laundering (AML) is a primary application of AI in risk management. AI-enabled transaction monitoring provides several benefits over manually defined, rules-based approaches. In addition to reducing false positive rates, AI systems can rapidly aggregate and analyze data — including transaction, account, company, customer relationship and other data — automatically prioritizing the highest-weighted money laundering risks. By assigning risk scores to transactions or customers based on factors such as transaction amount, frequency and nature of the transaction, teams can prioritize their investigations.

Accurately pricing risk during insurance underwriting

Putting a precise price tag on risk is essential for insurers to remain profitable and competitive. AI algorithms provide superior segmentation, allowing insurers to segment their customer base into more granular risk categories, resulting in more accurate, highly personalized pricing based on individual risk profiles.

Advance Your AI-Enabled Risk Management Program with Snowflake

Snowflake helps financial services organizations build a robust, scalable AI-enabled risk management program, with the tools and data infrastructure required to securely build and deploy ML models.

Build features, train and deploy your AI-driven risk management models

A range of risk management activities can be built and deployed through advanced ML models for a range of risk management activities. With Snowpark ML, you can quickly build features, train models and deploy them into production — all using familiar Python syntax and without having to move or copy data outside its governance boundary. The Snowpark ML Modeling API makes it easy to develop models, and the Snowpark Model Registry (in public preview) simplifies managing and governing your models at scale.

Secure, end-to-end support for containerized AI/ML models

With Snowflake, you can effortlessly deploy, manage and scale containerized models and fine-tune open source large language models (LLMs) using secure, Snowflake-managed infrastructure with GPUs. Snowpark Container Services, now in private preview, eliminates the need for users to deal with complex operations of managing and maintaining compute and clusters for containers. With containers running in Snowflake, there is no need to move governed data outside of Snowflake to use it as part of the most sophisticated AI/ML models.

Snowflake Cortex enables you to analyze text data and build AI applications with fully managed, industry-leading AI models, LLMs and vector search. With Cortex, you can leverage high-performing LLMs from Mistral AI, Meta, Google and more via SQL or Python serverless functions. Optimized compute infrastructure allows models to run securely without moving governed data.

In addition, Snowflake Arctic provides enterprise-grade models ready-built with a unique dense-MoE (mixture of experts) hybrid architecture that delivers top-tier results at a fraction of the development cost of comparable models. Arctic comes with an Apache 2.0 license, with ungated access to weights and code paired with open data recipe and research insights.

Snowflake is designed for AI. Unlock cutting-edge features to accelerate your AI-enabled risk management initiatives, improve risk intelligence and decision-making, and build a competitive advantage in a rapidly evolving financial services industry.

Using AI in Risk Management for Stronger Financial Services