Using AI to Detect Financial Fraud While Protecting the Constituent Experience

Using AI to Detect Financial Fraud While Protecting the Constituent Experience

State and federal agencies have invested millions in modernizing the systems that help constituents apply for and receive government assistance – and scammers have kept pace. Between 2018 and 2022, the government lost an upwards of $521 billion to fraud, according to the Government Accountability Office.

As bad actors’ strategies evolve, government agencies must consider both sides of the fraud equation. This means developing the necessary tools to catch criminals while also helping fraud victims resolve issues quickly and easily. If the pathway to resolution is difficult or time-consuming, it undermines the trust of taxpayers and leaves the agency in the dark regarding what went wrong.

Most state agencies understand that artificial intelligence (AI) and machine learning (ML) can be leveraged in the fight against the fraudsters. AI is well-suited for detecting fraud, as it can analyze past instances of fraud and flag new filings that appear suspicious. For example, if multiple claims are associated with a single username, email address, bank account, or IP address, this could be identified and flagged using graph-based analysis. At the same time, ML-based pattern recognition is well-suited for uncovering fraud clusters. From these models, risk scores are created that prioritize cases for investigation, based on an agency’s unique business rules.

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However, we have seen many agencies unintentionally deploy a cure that’s worse than the disease by ruining the constituent experience for end users who have been victims of fraud. If the process of interacting with an agency to resolve a fraud incident is difficult or time consuming, the user will become frustrated, further damaging their trust in the agency. Plus, a lengthy resolution process is counterproductive for the agency. As long as the situation is unresolved, the agency can’t use lessons learned for  future decision making or further integration into prevention and detection processes.

Fortunately, AI/ML can also be used to continuously monitor and analyze user friction to ensure more seamless interactions with government systems and processes.

For example, AI chatbots, driven by improvements in natural language processing, can streamline the resolution workflow and reduce customer friction, while also freeing up humans to handle issues that demand greater emotional intelligence. Ideally, agencies should let customers set their level and type of engagement to ensure a positive customer experience.

All in all, the ultimate measure of a successful fraud prevention program is a reduction in fraudulent attempts. Such reductions are possible: with technological improvements, identity theft has declined more than 70% over the past five years at the IRS. And yet, bad actors are always creating new schemes and approaches to financial fraud. Thus, agencies must continually reassess their technology and strategy to ensure residents are protected and that fraud controls are balanced with a frictionless customer experience.

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-Voyatek Leadership Team