A CIO's Guide to Fraud Detection in Banking: Buy, Build, or Integrate

Key Highlights 

• Fraud detection in banking can be strengthened through three main approaches: buying a vendor platform, building custom AI models, or integrating an AI layer into existing systems.  
• The best option depends on a bank's infrastructure, data maturity, internal expertise, governance requirements, and speed-to-value expectations.  
• For many institutions, enhancing existing systems with AI offers a balanced path to better protection against evolving financial crime without the cost, risk, and disruption of a full replacement or custom build. 

What Is the Best Approach to Strengthening Fraud Detection in Banking?

With fraud prevention and security now ranking among banks' top investment priorities, leaders are under growing pressure to act. Most banks evaluating their options face three paths: buying a vendor platform, building custom machine learning models from scratch, or integrating a targeted AI-powered layer alongside existing systems. 

Each approach addresses different challenges and comes with its own trade-offs. The right choice depends on factors such as the bank's existing technology landscape, data maturity, internal expertise, and capacity for change. 

This guide provides a practical framework for evaluating these options and selecting the approach that best aligns with your organization's needs, before any vendor is in the room. 

Option 1: Buy a Vendor Platform 

Buying a pre-built vendor platform offers the fastest path to improved fraud detection capabilities. Banks get fraud models, workflows, rules engines, dashboards, and case management tooling out of the box. For institutions facing urgent remediation timelines, that speed is genuinely valuable. However, many banks underestimate the long-term constraints that can emerge around customization, governance, and vendor dependency. 

Imagine you're leading a mid-sized retail bank that chooses an off-the-shelf anti-fraud platform for speed. The solution is up and running within months and delivers quick wins. But as fraud patterns evolve, the bank needs changes that fall outside the platform's standard capabilities. What initially felt like flexibility starts to create delays, making it harder for the bank to respond quickly to evolving threats. 

Benefits  

• Fastest deployment timeline of the three approaches 
• Access to prebuilt fraud models, rules engines, and workflows 
• Built-in dashboards, monitoring, and case management capabilities 
• Reduced need for in-house machine learning expertise 
• Faster time-to-value for banks facing urgent fraud challenges 

Potential limitations 

• Limited visibility into how models make decisions 
• Greater dependence on a single vendor ecosystem 
• Restrictions on customizing models for institution-specific fraud patterns 
• Reduced flexibility to adapt detection logic independently 
• Ongoing reliance on vendor roadmaps and support teams 

This approach is a good fit for 

• Banks with fragmented or outdated fraud tooling that requires significant replacement 
• Institutions without internal ML expertise and no practical path to building it in the near term 
• Organizations facing regulatory or business pressures that demand rapid deployment 
• Banks whose primary fraud challenges are well-covered by standard vendor models 

Option 2: Build a Custom Fraud Detection Model Using Machine Learning 

Building a custom machine learning fraud detection solution gives banks the highest level of control. Models can be tailored to specific fraud patterns, data sources, and risk requirements. Keep in mind this approach requires significant investment, specialized expertise, and ongoing maintenance.  

When executed well, the results can be substantial. For example, JPMorgan Chasedeveloped the Account Confidence Score (ACS), a proprietary AI-powered fraud risk model trained on more than 15 billion payment transactions. The solution helps assess fraud risk before a payment is initiated, demonstrating how banks can leverage their own data and in-house AI capabilities to prevent financial crimes at scale. Beyond reducing fraud losses, such capabilities can also improve customer trust and retention - a win for both the bank and its customers. 

Benefits 

• Full control over models, rules, and decision logic 
• Ability to tailor detection capabilities to institution-specific fraud patterns 
• Greater transparency and explainability 
• Flexibility to evolve models as fraud tactics change 
• Reduced dependence on external vendors 

 Potential limitations 

• Longest implementation timeline of the three approaches 
• Requires experienced data science, engineering, and MLOps capabilities 
• Higher upfront investment and ongoing maintenance costs 
• Greater responsibility for governance, monitoring, and compliance 
• Risk of delayed value if requirements or priorities change 

This approach is a good fit for 

• Banks with mature data and engineering capabilities 
• Institutions facing fraud patterns that cannot be addressed by standard solutions 
• Organizations with strict requirements for transparency and control 
• Banks that seek advanced security capabilities to differentiate themselves in the market  

Option 3: Add an AI-Powered Layer to Existing Fraud Detection Systems 

If you want to modernize fraud detection without replacing existing platforms, one option is to add AI-powered capabilities alongside your current systems. This allows banks to improve detection while preserving existing investments, processes, and workflows. 

Such an approach can be particularly effective when existing fraud platforms still perform well in core areas but struggle to detect emerging patterns. A good example comes from our work with Castle Trust Bank, where we developed a proof of concept for an AI-driven fraud detection solution. The system used predictive analytics to help identify potentially fraudulent transactions in real time, complementing the bank's existing fraud controls. 

For organizations looking to accelerate implementation, but wanting a balanced approach, pre-built AI solutions can reduce deployment timelines significantly compared to custom development. For instance, we have developed an AI-powered fraud detector that can be deployed alongside your existing fraud platform. The solution scores transactions based on fraud probability, automatically resolves low-risk cases, and flags higher-risk transactions for review. 

Benefits 

• Faster implementation than a full custom build 
• Lower disruption to existing operations and workflows 
• Ability to enhance detection without replacing core systems 
• Greater flexibility than many off-the-shelf platforms 
• Incremental adoption with lower transformation risk 

Potential limitations 

• Success depends on the quality and accessibility of existing data 
• Integration complexity can vary across environments 
• Existing process inefficiencies may remain in place 
• Requires ongoing oversight and governance of AI models 
• May not address deeper limitations of legacy infrastructure 

This approach is a good fit for 

• Banks with established fraud management systems that still provide value 
• Institutions looking to improve detection accuracy without a major transformation program 
• Organizations seeking faster results with lower implementation risk 
• Banks that want more flexibility without building everything from scratch 

How To Choose Between Buying, Building, or Integrating Fraud Detection Technology? 

Before deciding whether you should buy, build, or integrate fraud detection technology, assess those five criteria:  

• Infrastructure maturity: Can your systems support real-time inference? Are fraud, transaction, and customer data environments integrated enough to support a new layer without significant upstream work?  
• Data readiness: Do you have enough accessible, high-quality data to work with? Is the data sufficiently structured, labeled, and available for analysis or model training?  
• Regulatory and governance requirements: How important is model explainability? How much third-party model dependency is acceptable within your governance framework? The answers can significantly influence whether a bank is comfortable relying on vendor models or requires greater internal control. 
• Operational tolerance: How much change can your organization realistically absorb? The technically optimal solution is not always the operationally realistic one.  
• Strategic objective: Some banks need to address a specific risk management challenge while others want to redesign fraud operations more broadly. The goal determines which approach is the best fit, and which options can be ruled out early. 

Which Decision Path Is Right for You? 

The pressure to strengthen fraud detection in banking is growing, but there is no one-size-fits-all approach. Buying, building, and integrating all have their place. The right choice depends on your existing systems, data, internal capabilities, governance requirements, and capacity for change. 

If you're not sure which path is right for your organization, start by assessing the criteria outlined above. They will help you get a clearer picture of where you stand today. If you'd like to discuss your options, our financial services team can help you identify potential gaps and find the most practical way to strengthen your fraud detection capabilities.