Gen AI Use Cases for AML, Making BaaS Compliant, and Entity Risk Detection

How Will Gen AI Improve the Fight Against Financial Crime?

Wolfgang Berner, Chief Product Officer & Co-founder at Hawk

You probably don’t need another introduction to what Generative AI is, so let’s cut right to the chase – Gen AI offers huge potential for financial institutions that want to increase risk coverage and reduce the cost of their anti-financial crime operations. How? Let’s start with four key use cases:

Detecting sanctions violations

⧁ Gen AI understands language – it’s able to detect synonyms, clues and more in transaction and trade data

⧁ This means it can reduce false positives – like a human investigator, it can tell if a word only looks risky or really means suspicious activity

⧁ At the same time, this helps detect more crime – Gen AI can find dual-use or slang terms to find hidden risk

Detecting money laundering and fraud

⧁ Instead of a Large Language Model, think of a Large Transaction Model – Gen AI can be trained to understand transactional behavior at its deepest level

⧁ Always learning, it can identify complex correlations between transactional attributes, detecting hidden cases of money laundering and fraud within vast datasets

Aiding case investigations

⧁ Gen AI as a ChatGPT-like co-pilot can help investigators add additional contextual information to a case and create high-quality case narratives for reports

⧁ Human oversight and validation, of course, remain crucial in this process

Improving QA processes

⧁ Gen AI can help financial institutions streamline their operations and teams

⧁ By analyzing case outcomes, it can identify where teams need training or where tooling and processes need to be aligned – additionally, Gen AI can create more visibility for stakeholders by automating summary reports

Read more in the full article. Check it out here.

More from Hawk

Introducing Entity Risk Detection

How can financial institutions join the dots in their datasets to detect more risk and improve the quality and efficiency of investigations? Entity Risk Detection, the new solution from Hawk, helps you build a clearer, richer picture - for example, to see that Customer A using Product X is the same person as Customer B using Product Y, and that Customer B is connected to Firm C, which is a sanctioned entity. The result? Increased risk coverage and investigation efficiency.

Entity Risk Detection enables FIs to:

⧁ Consolidate and harmonize various datasets 

⧁ Connect data points and resolve entities 

⧁ Detect risks in the newly optimized data

⧁ Carry out network analysis

⧁ Ensure AI is powered with good data

Read more here.

Solving BaaS Challenges With AI-powered AML Tech

The Banking-as-a-Service (BaaS) ecosystem is coming under increased scrutiny from regulators. How can banks, fintechs, and BaaS providers ensure they manage risks while taking advantage of the growth opportunities that BaaS offers?

A modern AML technology solution enables: 

⧁ Sponsor banks to effectively employ AI models to efficiently monitor for potentially suspicious activity flowing from their partners

⧁ Fintechs and banks leveraging BaaS technology to manage associated risks properly

⧁ BaaS providers, regardless of regulatory status, to maintain strong compliance programs

Read more here.

Hawk in flight:

⧁ Datos FinCrime Forum, Charlotte, NC, August 27-28

⧁ FinCrime Summit DACH, Frankfurt, September 5

⧁ ACAMS Vegas, Las Vegas, NV, September 23-25

⧁ AML & FC Conference, London, November 11-12

Find out what our AML and fraud detection technology, powered by explainable AI, can do for you:

⧁ Transaction Monitoring

⧁ Payment Screening

⧁ Customer Screening & pKYC

⧁ Transaction Fraud Prevention

⧁ Entity Risk Detection

Request a demo here.