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The BaaS Challenge: How AML Tech Facilitates Growth While Maintaining Compliance

How AML Tech Facilitates Growth While Maintaining Compliance

By Chris Caruana, VP of Strategy

The technological infrastructure that Banking-as-a-Service (BaaS) provides has become a valuable proposition for banks and fintechs. These engagements are born out of the perpetual search for growth opportunities in an increasingly digital economy.  
The benefits of partnering with BaaS providers are numerous.  A primary component is the expansion of products and services in an innovative, competitive, and flexible manner not otherwise accessible.  This can transform a BaaS partner’s offering, making them more attractive to consumers and businesses demanding more digital banking services.  It also enables a sponsor bank to reach a wider audience more efficiently. 

It is equally important we do not walk past the risks of these partnerships.  Recent regulatory announcements (enforcement actions and consent orders), fines, and reputational damage are the tip of the iceberg for those that do not maintain a proper control environment. Appropriate third-party lifecycle management practices, coupled with effectively employed, advanced anti-financial crime technology are necessities for sponsor banks and the consumers (fintechs and, at times, other banks) of BaaS technology alike.  BaaS providers would do well to offer compliance solutions and maintain adequate regulatory programs as well, if they are not already. 

Flow chart showing the monitoring relationship between BaaS Consumers, BaaS Providers, and Sponsor Banks

In this article, we will discuss what recent consent orders mean for bank-BaaS-fintech partnerships, how robust AML technology helps financial institutions manage BaaS-related AML risk, and how AI improves AML risk management.

Key Takeaways

  1. Bank-BaaS-fintech partnerships need stronger AML controls
  2. Robust AML technology can help sponsor banks identify more risk using fewer resources
  3. AML technology can help BaaS providers add value to their offering
  4. Better AML technology can help banks and fintechs address compliance needs
  5. Explainable AI improves AML risk management by… 
    • Increasing efficiency via false positive reduction, streamlined processes, and transparent decisioning
    • Increasing effectiveness via anomaly detection, robust case management, and improved compliance
  6.  AML technology helps banks, Baas providers, and fintechs to…
    • Work together to bring products to market 
    • Create and maintain appropriate AML risk management controls
    • Gain a 360-degree view of customer risk 

What Do Recent Consent Orders Mean for Bank-BaaS-Fintech Partnerships?

Recently, regulators like the FDIC and OCC have delivered multiple enforcement actions against banks engaged in BaaS partnerships. These orders make it clear that the ultimate responsibility for AML compliance and risk management lies with the regulated institutions. However, everyone has a role to play when it comes to overseeing the third parties they work with (especially when it comes to their own controls). This means that banks and regulated fintechs need appropriate controls in place to manage the AML risk inherent to working with BaaS partners. Banks who proactively adapt their AML programs for these partnerships with fit-for-purpose technology will better position themselves to take advantage of BaaS business opportunities without incurring additional risk.

The global fight against financial crime requires increasing coordination amongst participants. Financial crime compliance is a business driver and competitive differentiator for BaaS providers in a growing competitive landscape. Trust and security are valuable propositions to any partner, especially those with regulatory pressure.

How Can AML Technology Help Sponsor Banks Manage Risk?

At the time of this writing, most of the scrutiny and regulatory action is focused on sponsor banks. If you work for a sponsor bank, this means that, more than any of the other actors in the ecosystem, you need to review your AML technology. The challenge of monitoring customer and transaction data from multiple different entities and financial institutions is considerable. This is where AI-powered technology can help: with the right solution, sponsor banks can make sense of and analyze the data, detecting more risk faster—and with fewer resources. This improves risk coverage and makes it less likely that you will see regulatory action.

One of the risks with the BaaS model is that sponsor banks can become removed from the end customers of their banking products, making it hard to monitor for suspicious activity. However, regulators will still hold the sponsor bank responsible for ensuring that this activity is carried out. In this situation, requiring BaaS, small bank, and fintech partners to use the same AML technology downstream can make monitoring and reporting processes more efficient. When every partner in the ecosystem has appropriate risk management and AML technology in use, risk mitigation is significantly enhanced. 

To facilitate monitoring of downstream customers and transactions, modern AML technology is built with a multi-tenant hierarchal structure. This means that data can easily flow up to the parent tenant (the sponsor bank) while remaining siloed between child tenants (BaaS providers) and grandchild tenants (banks and fintechs). See the graphic below for a visualization of this structure. This structure helps sponsor banks partner with multiple BaaS providers and their end clients while monitoring downstream activity and maintaining strong data security. All of this serves to enhance the sponsor bank’s AML processes. 

Diagram showing relationships between Parent Tenants (Sponsor Banks), and child (BaaS Providers) and grandchild tenants (banks and fintechs)

How Can AML Technology Help BaaS Providers Manage Risk?

While BaaS providers may not currently have the same regulatory responsibility as their sponsor bank partners in the US, a day of reckoning is likely coming. Regulators are focusing on these partnerships and the control frameworks that underpin them. They may well require BaaS providers who aren’t already regulated to acquire a license. If you are a BaaS provider, it’s important to consider how you can integrate AML controls into your technology infrastructure and operations. You also have a unique opportunity: when you integrate AML technology into your technology ecosystem, you can bundle AML monitoring with your other BaaS offerings to provide additional value to banking clients. 

How Can AML Technology Help Banks Manage Risk?

If your bank uses the services of a BaaS provider, you face your own unique regulatory challenges. Chances are, if you work for a smaller sized bank, then you won’t have extensive technological resources that you can deploy to manage this challenge—in fact, one of the benefits of the BaaS model is that it enables smaller banks to offer more products and services to their customers in a cost-efficient way. It's crucial to do this without increasing the need for oversight resources. However, it’s challenging for legacy rules-based AML technology to keep up with evolving regulatory requirements and emerging risks. A modern AML solution can help you maximize your limited investigative resources, detecting more risk and minimizing false positives. When you use this technology, you improve your AML risk coverage without ballooning costs.

How Can AML Technology Help Fintechs Manage Risk?

Fintechs looking to offer banking products via BaaS partners will find themselves in a similar position to banks, albeit without the existing regulatory requirements to manage. This can, at times, lead to complacency and misalignment with the need for overseeing customers and their activity. With limited resources focused on investigation and AML oversight, fintechs might need to a) partner with a BaaS provider that offers AML technology, or b) use cost-efficient AML technology themselves. In either case, AML technology will help fintechs improve risk detection in an effective and efficient manner.

How Can AI Technology Help Manage the AML Risk Inherent to BaaS-Bank-Fintech Partnerships?

Banks and BaaS providers alike should take advantage of AI’s ability to significantly improve risk coverage, regulatory compliance, and operational efficiency. AI helps banks manage risk by improving the efficiency and effectiveness of their AML systems and processes.

Increased AML Efficiency

AI helps banks improve the efficiency of their AML systems and processes with the following benefits:

  • Reduced False Positives: As AML teams scale their operations, they can become overwhelmed by the increase in volumes of false positives. AI can significantly reduce false positives by applying intelligent, context-aware filters to reduce irrelevant noise in customer and transaction data, focusing a bank’s investigative attention on real risk. 
  • Streamlined Processes: AI can streamline AML processes by automating transaction monitoring, prioritizing alerts, and enabling rapid deployment of new detection rules. This helps banks reduce AML compliance costs. It also empowers banks to quickly respond to regulatory changes and emerging threats. With streamlined processes and enhanced responsiveness, banks can more effectively manage regulatory and reputational risk.
  • Transparent Decisioning: Explainable AI models optimize regulatory compliance by delivering natural language rationales and risk scores with decisions. This facilitates the explanation of decisions in audits and regulatory exams, ensuring transparency and human oversight in AML processes. 

Increased AML Effectiveness

AI helps banks improve the effectiveness of their AML systems and processes with the following benefits:

  • Improved Risk Identification: AI-powered anomaly detection and pattern recognition identify suspicious activity that would otherwise go unnoticed. The ability to ingest large volumes of data from BaaS providers, analyze it, and detect suspicious activity is essential for sponsor banks with a regulatory mandate. This AI use case helps banks to improve AML risk coverage and prevent regulatory action.
  • Robust Case Management: AI-driven case management provides a 360-degree view of customer risk to AML teams. AI models automatically pull relevant customer data from disparate sources and aggregate it into a single case. This allows banks to make more accurate risk assessments while using fewer investigative resources. 
  • Improved Regulatory Compliance: More effective risk identification, reduced false positives, robust case management, transparent decisioning, and streamlined processes all combine to improve compliance with AML regulations. 

How Do Banks, BaaS Providers, and Fintechs Use Robust AML Technology to Manage Risk?

Let’s consider an example.

A community bank partners with a BaaS provider to provide a new instant payments product to the bank’s customers. The BaaS provider relies on a sponsor bank to provide licensed banking products. The sponsor bank has modern, AI-powered AML technology in place that can monitor activity down the chain, ensuring that transactions and customers using the community bank’s instant payment product are covered.  

The banks and BaaS provider use AI-powered AML technology to screen customers and payment counterparties, monitor transactions, and produce customer risk ratings (also known as perpetual KYC, or pKYC). As transactions occur, the AI models flag suspicious activity using anomaly detection and pattern recognition. Human investigators at both banks review the AI's findings in a case manager that aggregates relevant contextual information. Simultaneously, the AI models analyze rule-based alerts and prioritize them for investigators with behavioral analytics, reducing false positives (and therefore costs). 

The AI models deliver human language explanations to the sponsor banks’ investigative teams for every decision they make. The bank can now easily file suspicious activity reports (SARs) and defend their AML controls and risk-based approach to regulators in their periodic exams.

Both banks and the BaaS provider have thereby unlocked a valuable partnership from the BaaS model, while protecting themselves from potential regulatory actions and reputational damage. Using AML technology helped them minimize their compliance costs and improve their risk coverage. 

Diagram showing the BaaS Regulatory Ecosystem

Hawk’s AI-Powered AML Risk Management Technology

Banks and BaaS Providers can use Hawk’s AI-powered technology platform to improve their AML and Fraud risk coverage via anomaly detection, pattern recognition, and false positive recognition. Our technology is configurable, flexible, and scalable, allowing banks and BaaS providers to manage and respond to emerging AML risks in bank-BaaS-fintech partnerships. Hawk’s technology features a multi-tenant hierarchal structure that facilitates partnerships between banks and BaaS providers.

To learn more, contact us today.

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