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Introduction

One of the biggest enemies of the modern way of life is the rising threat from financial fraud. Research has shown that across the globe, eCommerce companies alone lost over USD 41 billion due to payment fraud in a single year. And that’s just one type of digital fraud in one sector. Estimates for the true scale of financial fraud range from the merely enormous to the truly unthinkable! As more customers hop into the digital bandwagon and banking and financial pathways open up, there is a heightened need to ensure safety and security for transactional engagements and processes.

For years, banks, financial institutions, and other businesses have deployed a range of fraud prevention technologies in their workflows and processes to combat this threat. Two of the major approaches that most businesses take are deploying rule-based and AI-enabled fraud prevention measures. While both have their advantages and limitations, they have both settled in to be widely used by different segments of businesses. It may be time to change that though.

Over time, the complexity and scale of financial fraud has grown beyond imagination. This calls for an approach that combines both rule-based and AI models to jointly fight off the threat of fraud.

Combining the forces

When AI models and rule-based fraud prevention techniques are combined, financial enterprises get a stronger and more informed weapon to fight potential dangers. It helps them transition from relying on just a first line of defense in the form of rules to building a comprehensive end-to-end security framework. The aim is to autonomously create a robust security ecosystem by accommodating threat trends as well as ensuring that customer trust is protected at all times with rule-based enforcement at grassroots levels of monitoring.

How can organizations build rule-based AI models to combat financial fraud?

Organizations need to adopt a strategic approach in combining rule-based and AI models to build an effective anti-fraud solution for financial operations. Let us explore some fundamental tips to build a fail-proof hybrid mechanism using the two:

Assemble a core team

Discovering threats is essential to the success of any anti-fraud measure. In a combined solution approach, defining the fraud prevention rules is a very crucial moment. For this, businesses need to bring together security analysts, data engineers, data science professionals as well as business domain experts to collaborate and identify sample rule sets. The parameters used in sample rulesets are the foundation elements for screening any transaction through the new hybrid fraud prevention system. Over time, AI models can learn from the rule sets and evolve the defense to higher levels.

In short, manufacturers can transform their business model from being one driven by single purchases to one managed as a continuous subscription program. It is similar to how SaaS technology works. In this case, the product is offered as a service.

They can constantly leverage PTC ThingWorx to build a connected oversight dashboard for products. The dashboard gets the harnessed data from products at customer locations. Remote diagnostics and repair of problems, continuous usage feedback monitoring, and a better understanding of use cases for future design inputs are major advantages in this scenario.

Build an interactive rules development engine

The rules developed must serve as the foundation for further AI-enabled growth of the anti-fraud solution. Hence it must be able to provide collaborators with a simple-to-use dashboard or portal that allows them to collate and work on historic data trends. These dashboards help in building rules that can further be made available for exploratory analysis in AI models. This dashboard for rule configurations can also be used to refine and modify new rule inferences that AI models create over time with learning.

Create a data pipeline

A hybrid fraud prevention system using rules and AI models has the advantage of being able to offer protection against fraud with hard-coded rules quickly. Building AI-enabled services in any segment requires the use of massive amounts of historical data to train and perfect operational models. As the system evolves, AI models can learn to expand rules autonomously based on threats experienced. For a smooth interoperability between rules and the AI system, it is essential to have a data pipeline that serves both. Training data, event output data, rule validation data, etc. should be seamlessly made available through this pipeline for AI systems to pick up pace. This data pipeline acts as a single source of truth for the entire anti-fraud prevention ecosystem to work and evolve.

Create an autonomous decision framework

Once the rules and subsequent AI models are defined, then it is time to operate the autonomous decision-making framework for preventing fraud. This system must work in real-time and validate not just rule-based variations in behaviour of transactions, but also ensure that AI models can run subsequent computations to determine if they are genuine. All this needs to happen without human intervention and hence this is a very crucial step. Over time, when the data to be analyzed for fraud is huge, this step could become a major bottleneck if not provided with the right resources.

Combating financial fraud is an evolving and continuous process that enterprises must undertake as a strategic growth pillar. As more digital transactions become a mainstay, fraudsters will look at every opportunity to strike and create damage. Discovering and eliminating vulnerabilities in your digital channels is a great way to minimize the risks of financial fraud. A hybrid approach that combines rules and AI models is the perfect way to sustain a long-term fight against such modern-day fraud.

Having the right arsenal of tools and knowledge to stay vigilant could prove to be the biggest advantage for your business.

There are various tools available that can be leveraged to deploy rule based AI models. Databricks has been gaining popularity recently with its advanced features and a comprehensive intelligent data and AI platform capabilities infused with GenAI functionality to help organizations address complex use cases with ease. An experienced Databricks partner like Pratiti can help in building the most sustainable protection framework for your financial ecosystem. Get in touch with us to learn more about building a rule-based AI model to combat threats and financial fraud.

Nitin
Nitin Tappe

After successful stint in a corporate role, Nitin is back to what he enjoys most – conceptualizing new software solutions to solve business problems. Nitin is a postgraduate from IIT, Mumbai, India and in his 24 years of career, has played key roles in building a desktop as well as enterprise solutions right from idealization to launch which are adopted by many Fortune 500 companies. As a Founder member of Pratiti Technologies, he is committed to applying his management learning as well as the passion for building new solutions to realize your innovation with certainty.

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