The Evolving Role of Banks in Fraud Detection and AML: Why Smarter Collaboration and AI Are the Future

As financial crime grows in complexity, banks across the globe—particularly in fast-developing regions like Asia Pacific—are being called to play a much larger role in the fight against fraud, money laundering, and cybercrime.

No longer just providers of financial services, institutions today are de facto crime prevention hubs. But this new mandate comes with mounting costs, stricter regulations, and increasing operational challenges. In this context, a recent discussion with Nick Lewis, Managing Director of the High-Risk Client Unit at Standard Chartered Bank, offers timely insights into how financial institutions can respond to this shifting landscape with smarter collaboration, more robust data sharing, and the intelligent application of technology.

Rising Financial Crime, Rising Expectations

According to the FBI’s 2024 Internet Crime Report, cybercrime complaints reached nearly 860,000 incidents, with associated financial losses soaring to $16.6 billion. While this data reflects U.S. trends, it mirrors similar concerns across Asia Pacific, where rapid digital adoption, fintech expansion, and patchy enforcement have left gaps in financial crime detection and prevention.

In regions like Southeast Asia and South Asia, regulators are placing more responsibility on banks to lead fraud investigations and enforce AML compliance. However, many institutions are still reliant on outdated monitoring tools, manual investigations, and siloed data systems that can’t keep pace with emerging threats.

1. A Shift Toward Public-Private Collaboration

One of the clearest messages from Nick Lewis: better collaboration between financial institutions and law enforcement is essential.

Historically, banks shared information only when compelled to. Law enforcement agencies, on the other hand, often lacked clarity on what financial data could be accessed and how to request it effectively. The result? Fragmented investigations, missed connections, and lost time.

Now, the model is evolving. More institutions are proactively offering intelligence—provided that privacy laws and regulatory constraints are respected. This shift is leading to earlier interventions, better investigations, and increased trust.

Despite this, a recent University of Portsmouth study found that of 75 global public-private initiatives focused on economic crime, only 7 emphasized joint detection and investigative work. For countries across Asia Pacific, building meaningful public-private partnerships is still in its early stages, though there are bright spots—such as Singapore’s collaborative ACIP framework and Australia’s financial intelligence alliances.

2. From Alert Fatigue to Holistic Risk Intelligence

Most banks still rely heavily on traditional rule-based transaction monitoring systems that produce high volumes of alerts—most of which turn out to be false positives. This “alert fatigue” ties up resources, undermines investigation quality, and slows down responses to real threats.

The future lies in contextual analysis.

Lewis advocates for a holistic approach that integrates:

• Internal customer data (transaction history, behavioral patterns)

• External signals (watchlists, corporate registries, sanctions data)

• Government intelligence (ongoing investigations, suspect networks)

When these data layers are fused together, banks can assess customer behavior with far more precision, reducing false positives while surfacing genuinely suspicious activity faster.

In parts of Asia, some institutions have already begun to implement such approaches. For example, behavioral biometrics and device intelligence are being used to enhance traditional KYC and reduce fraud at onboarding stages.

3. Artificial Intelligence: Offense, Not Just Defense

Criminals are using AI to automate phishing attacks, generate fake identities, and mimic user behavior. Financial institutions need to respond in kind—not just to stay compliant, but to stay competitive.

According to Lewis, AI should be viewed not only as a cost-cutting tool, but as an intelligence enabler. It can be used to:

• Detect transaction anomalies in real-time

• Identify previously unseen fraud typologies

• Connect the dots across siloed datasets

• Provide predictive risk scoring

This is already being explored in emerging markets, where fintech adoption is outpacing traditional infrastructure. For instance, AI has been deployed in India to uncover mule networks operating across mobile wallets, while banks in Indonesia are using machine learning to identify dormant accounts used for fraud rings.

The key is not just to automate—but to anticipate.

4. Educating Law Enforcement and Building Trust

A major challenge in many jurisdictions is that law enforcement lacks understanding of what banks can offer—both in terms of data granularity and legal access.

Banks often receive requests for simple transaction lists when what’s actually needed is metadata: patterns, timeframes, devices used, velocity, and context. Training law enforcement to understand these dimensions—and enabling secure access—can dramatically improve investigative outcomes.

This educational effort is starting to gain traction in places like Australia and Hong Kong, where joint workshops and data-sharing playbooks are bridging the gap between compliance teams and police forces.

As Lewis puts it: "It’s a big hill to climb to get law enforcement to trust a bank. But slowly, we’re getting there.”

5. Beyond Banks: The Broader Financial Ecosystem

Fraud and financial crime don’t stop at the borders of banking. Payments companies, telecom providers, e-commerce platforms, and even social media firms now play a role in either enabling or preventing criminal activity.

In response, some jurisdictions are building multi-sector financial crime task forces. These cross-industry coalitions bring together data from across the digital economy to provide a more complete risk picture—and enable faster responses.

As new threats like deepfake identity fraud and synthetic accounts emerge, this multi-sector cooperation will only grow in importance.

Looking Ahead: Redefining Compliance as a Strategic Capability

The fight against financial crime is no longer about regulatory box-ticking. It’s about building a real-time, intelligence-driven ecosystem that can detect, deter, and disrupt criminal activity—often before it fully materializes.

Key priorities for financial institutions across Asia Pacific and beyond should include:

• Investing in data fusion and AI-driven detection

• Collaborating openly (and securely) with law enforcement

• Educating government agencies on financial system capabilities

• Engaging across the broader fintech and digital commerce ecosystem

In a landscape where fraudsters are fast, well-funded, and increasingly digital, the financial industry’s only winning strategy is to be faster, smarter, and more connected.