Harnessing AI for Effective Risk Management—Why Financial Institutions Need a Robust AI Infrastructure

Risk can be quantified, uncertainty cannot. Financial institutions deal with uncertainty by turning it into risk and then quantifying and managing that risk. Banks are required to maintain a certain asset level to cover potential losses based on the level of risk inherent in investments—and in the financial world, all investments are risky to a greater or lesser degree. The better risk is managed, the more assets the financial institution has available for other purposes such as investment or lending.  

It will come as no surprise that financial institutions are turning to AI to help manage risk (for example, Banca Mediolanum). 

Using AI Search for risk management is a powerful approach: AI can often find patterns and identify trends before they become obvious. Changes in a customer’s spending habits can be early warning signs of financial difficulties down the road, and financial institutions are in an excellent position to spot such changes. But using AI Search requires getting all the necessary data to the AI, and that can be a challenge. 

One approach, moving data to a vector database, can be slow and expensive. Having multiple copies of data creates synchronization issues and creates challenges identifying which version of the data is the most recent. Changing data formats when copying or moving data can cause data loss due to conversion limitations. Copying data outside the corporate firewall exposes the institution to the possibility of a data breach and may well violate regulatory guidelines.  

Taking Advantage of AI Search 

Fully taking advantage of the power of AI to manage risk requires AI search that: 

• Is AI model agnostic, allowing you to use the model that fits your needs and that is approved by your company.
• Is Zero-ETL, eliminating the need to move or copy large volumes of data across the network.
• Integrates with existing systems and security models. Large financial institutions already have complex technological ecosystems. Interoperability is crucial.
• Ensures security and compliance without adding yet another layer of protocols for users to remember.

Using AI Search effectively involves more than merely dropping an AI model into your ecosystem and expecting magic, even if it may seem that way at first. It requires creating the foundation that will support long-term growth and innovation in a dynamic technological landscape, thereby reducing uncertainty and controlling risk.  

SWIRL makes all that possible. 

Reducing Uncertainty with SWIRL AI Search 

SWIRL’s advanced AI Search provides significant practical benefits beyond quickly and easily finding the data you need when you need it: 

• Data stays in place: SWIRL enables AI to connect with source systems, bringing the AI to the data with zero ETL.
• Unified search: SWIRL simultaneously searches all data and content repositories that an employee has access to.
• Integration with existing security: SWIRL utilizes the source system security policies, following a zero-trust security approach to ensure users access only what they’re allowed to see.
• AI flexibility: SWIRL supports using multiple LLMs and AIs, including company-created models located behind the firewall.
• Data format agnosticism: SWIRL supports structured data from databases, as well as content from email, office documents, collaboration tools such as Teams and Slack, and more. SWIRL connects to over 100 source systems.
• Results you can trust: SWIRL’s innovative use of Retrieval Augmented Generation grounds results in facts, reducing or eliminating the risk of AI hallucinations and errors.

SWIRL enables financial institutions to get the maximum benefit from using AI Search. To find out more about how SWIRL can help your institution take advantage of the power of AI Search, contact us today.