How can you improve anti money laundering/know your customer compliance (AML/KYC) while reducing investigative costs and slashing fraud?
You can do it with agentic AI-powered financial crimes solutions. In fact — if you want to comply with current AML/KYC mandates and improve profitability — you have to do it.
Financial institutions, fintechs, and insurance companies face growing pressure to strengthen financial crime compliance as fraud, money laundering, and synthetic identity abuse continue to rise.
Artificial intelligence intertwines these crimes:
- AI makes it easier for money launderers to obscure their identities and enter national financial systems.
- It empowers fraudsters to scam banks and insurance companies with synthetic identities
AI has exacerbated instances of money laundering and fraud. But the right AI-powered financial crime compliance solutions can help businesses spot money launderers, fraudsters, and other criminals. Concurrently, these solutions can provide real business value — dramatically reducing costly investigative time, improving profitability, and bolstering customer satisfaction.
The financial crime landscape and compliance pressures
FIs, fintechs, and insurers scramble to keep up with evolving AML/KYC regulations. These regulations significantly expand financial crime compliance obligations, particularly for crypto-related firms, fintechs, and insurers operating across jurisdictions. Some of the latest are the United States Guiding and Establishing National Innovation for U.S. Stablecoins (GENIUS) act. It requires comprehensive AML and sanctions-compliance practices for crypto currencies and stablecoins.[1] In the European Union, the Authority for Anti Money Laundering and Countering the Financing of Terrorism, founded in 2024, has expanded AML regulations to cryptocurrencies.[2]
Increasingly, insurance companies are subject to similar AML regulations. The Financial Crimes Enforcement Network (FinCEN) and state insurance departments, for example, now lobby for stronger due diligence for life-insurance policy holders.[3] Certain insurance products, notably those with investment components, may be subject to sanctions screening.
These and other new mandates are levied at least in part to combat AI-enabled crimes. Synthetic identities are one example. Others include deepfakes. These are used by criminals to trick bank voice-identification systems, or to impersonate real people during live-video verifications conducted by some banks at onboarding. Similar AI tactics are used for insurance fraud. Fraudsters use AI to create fake medical bills or treatment notes. They generate photographs of car accidents that never happened. Fraud rings use AI to create seemingly authentic documentation across vast numbers of claims.
The screening challenge
To stop fraud and comply with AML/KYC mandates, AML and risk management teams must screen massive numbers of customers and transactions against a broad array of fragmented data sources. These data sources typically appear in different formats produced in different jurisdictions, and written in different languages. In this environment, siloed, outdated, and rules‑based screening technologies cannot provide the insight needed for identity verification and fraud detection.
Optimized name matching, entity resolution, and relationship-mapping capabilities empower FIs and insurers to separate meaningful risk indicators from background noise. Without it, AML and risk teams too often overlook critical identity insight while spending too much time manually investigating false-positive name matches. In the insurance industry, investigators find that high volumes of claims and policy data obscure instances of identity fraud and identity duplication.
In short, FIs, fintechs and insurers too often make high‑impact decisions based on limited insight. Without effective financial crime compliance, organizations face:
- Fines or criminal prosecution for failing to meet AML/KYC mandates
- Reduced profit because of AI-powered instances of fraud
- Suboptimal decision making based on incomplete insight
- Decreased levels of customer satisfaction that can occur when lengthy screening processes delay customer onboarding. When it takes too long to open an account or buy a policy, some customers may simply choose another organization.
How Babel Street can help
Spend less time juggling inconsequential data and more time acting on what matters. With Babel Street identity risk intelligence, FIs and insurers can optimize AML/KYC and improve fraud detection with automated, accurate and efficient name matching, entity resolution, and relationship-mapping capabilities.
Babel Street’s agentic ‘AI-as-a-Worker’ approach to KYC and fraud detection will enable analysts to deploy AI agents for multi-step intelligence workflows at machine speed. The Babel Street Risk Intelligence Platform traverses massive datasets to extract entities, detect risk, map relationships, and otherwise assemble intelligence. In doing so, Babel Street dramatically reduces false positives and the concurrent need for manual review. Automation capabilities enable organizations to batch process up to 10,000 identities simultaneously.
In the field of fraud, Babel Street’s AI-driven pattern recognition capabilities detect behaviors and connections that may not be apparent through manual analysis. At the same time, faster, more accurate identity verification enables speedier onboarding and claims payouts for legitimate customers.
Of course, AI is only as good as the data it searches.
As noted above, raw data is noisy, fragmented, and, often, duplicated or inaccurate. It originates in a broad variety of languages. It can be manipulated by bad actors using slang, emoji, coded language, and spoofed identities to conceal their identity and behavior. Structured and unstructured records rarely connect cleanly.
Babel Street turns chaotic publicly available information into verified contextual intelligence. We collect data from more than 143,000 sources worldwide, published in more than 200 languages. We translate insight into your language of choice. We then enhance data for elite tradecraft at scale, ensuring that all ingested data is enriched and fully auditable.
Clients working with Babel Street benefit from:
- Dramatic reduction in name-match false positives. This reduction slashes investigative costs and frees investigative time for more meaningful work.
- Improved ability to spot fraud. AI‑driven pattern recognition surfaces suspicious behavior in high‑volume operational environments.
- Heightened decision making. By developing holistic views of entities and the connections among them, organizations can make better-informed decisions.
- Improved customer satisfaction.
As AI makes it easier for fraudsters to steal and for money launderers to evade detection, strong financial crime compliance programs are essential for protecting organizations, customers, and revenue. With Babel Street identity risk intelligence, organizations can modernize financial crime compliance by automating AML/KYC screening, reducing false positives, and accelerating investigations. Working with Babel Street can help your organization more quickly and effectively spot, and stop, these wrongdoers.
Endnotes
1. Lian, Nellie, “Next Steps for GENIUS payment stablecoins,” Brookings, March 2026, https://www.brookings.edu/articles/next-steps-for-genius-payment-stablecoins/
2. European Council of the European Union, “Fight against money laundering and terrorist financing in the EU,” February 2026, https://www.consilium.europa.eu/en/policies/fight-against-terrorist-financing/#:~:text=The%20rules%20introduce%20an%20obligation,recommendations%20for%20countries%20to%20apply.
3. Board of Governors of the Federal Reserve System, “SECTION 1025.210—Anti-Money Laundering Programs for Insurance Companies,” accessed March 2026, https://www.federalreserve.gov/frrs/regulations/section-1025210-anti-money-laundering-programs-for-insurance-companies.htm
Disclaimer:
All names, companies, and incidents portrayed in this document are fictitious. No identification with actual persons (living or deceased), places, companies, and products are intended or should be inferred.