Money laundering no longer exists in the shadows. It is a trillion-dollar industry. According to Nasdaq Verafin’s ‘2026 Global Financial Crime Report’,an estimated USD 4.4 trillion in illicit financial activity occurred in 2025 across the globe. This includes interconnected criminal activities like drug and human trafficking and terrorist financing. The report further reveals that financial institutions are most concerned about staying ahead of emerging crimes, keeping pace with evolving regulatory expectations and overcoming legacy technology limitations.
It is evident that FIs understand the grave need to rethink the strategy needed to outsmart criminals. They are engaged in a high-stakes game of chess with criminals while anti-money laundering (AML) costs are spiralling. Outdated rule-based systems, periodic reviews and manual investigations cannot keep pace with opponents’ rapid moves. Sophisticated methods such as trade-based money laundering, crypto layering and synthetic identities are hard to spot in an exponentially growing haystack.
A McKinsey article notes that banks dedicate up to 20% of their full-time employees to financial crime activities, making it a large cost base. Yet, the financial industry can detect only a fraction of illicit money flow, compounding the challenge. According to LexisNexis Risk Solutions, financial crime compliance costs in EMEA reached approximately USD 85 billion, with most surveyed institutions reporting rising FCC expenditure. These statistics make a strong business case for banks to rethink their AML strategies. They must reduce cost, become smarter and more effective and ensure compliance while meeting customer expectations.
So far, AML solutions have only been considered a shield for regulatory compliance. However, banks must now upgrade to a risk-based and digital-first solution with real-time monitoring. Let’s look at some common challenges and how modern AML solutions address them.
Static rule-based systems
Older systems worked with static rules like “generate alerts if more than ten transactions of $1000 occurred in a day”. Unfortunately, sophisticated criminals have become adept at manipulating these predefined parameters. Also, static, rule-based systems generate a high rate of false positives that drain resources, increase investigation workloads and inflate compliance costs.
Hence, banks are now shifting from a rule-based to a risk-based approach (RBA) to strengthen their AML process. The RBA approach follows the latest recommendations of the Financial Action Task Force (FATF), which has urged countries and FIs to assess risks and apply proportionate countermeasures. Instead of a ‘one-size-fits-all’ solution, RBA allows countries flexibility within the FATF framework to implement measures according to the nature and level of their risks.
Modern AML solutions powered by artificial intelligence (AI) and advanced analytics detect suspicious patterns. Through real-time monitoring, contextual analysis and dynamic risk scoring, these solutions can significantly reduce false positives, streamline investigations, and enable compliance teams to focus on higher-risk cases. They change the game from a reactive mode to an intelligent, predictive mode that can foresee and prevent potential crimes.
Fragmented data sources
One of the key challenges banks face in FCC is working with fragmented data from diverse sources. Since such data may be structured or unstructured, it is tough to use automated data analysis or extraction tools on it. AI helps rewrite the script here. According to McKinsey insights, AI agents can gather, analyse and perform quality checks on diverse and unstructured data.
Modern AML strategies now enable banks to take a digital-first approach to data collection, starting with customer onboarding and know your customer (KYC) processes. Banks can now opt for KYC as a service (KYCaaS) solutions that automate customer onboarding with comprehensive verification, risk profiling, and AML screening. This approach reduces FCC risks while also enhancing customer experience.
Additionally, there is now an understanding of collaboration across FIs, regulators, law enforcement agencies and other stakeholders to combat FCC more effectively through collective intelligence. There are secure data-sharing frameworks enabled through privacy-enhancing technologies (PETs).
Weak reporting
A PwC analysis titled ‘Financial Crime detection in Financial Institutions’ found that approximately 21.3% of Suspicious Activity Reports (SARs) contained insufficient information or lacked meaningful data linkages. Modern AML solutions address this issue to a great extent. Natural language processing (NLP)-based solutions can automatically generate SARs with required traceability, which strengthens compliance and builds resilience into the whole ecosystem.
Not surprisingly, banks have begun upping their game to meet the FCC challenge through modern AML solutions. With financial crime now representing an equivalent of 3.8% of global GDP, they understand what’s at stake. The Nasdaq Verafin study quoted earlier found that 75% of FIs planned to increase their use of AI for financial crime detection. That’s an indicator of how the high-stakes game of chess will unfold in future, with AI being a major challenge and the greatest ally in the AML fight.
How Infosys BPM can help
Infosys BPM’sfinancial crime compliance solutions are designed with a holistic approach to FCC. Combining AI, RPA, machine learning and other advanced technologies, these future-ready, scalable solutions deliver tangible benefits to customers in terms of cost savings, operational efficiency, and regulatory compliance.
