The Hidden Fraud Economy: Why Synthetic Identities Demand a New Defense Strategy

By Uma Shankar Kulasekaran

Fraud has always been a chase. Banks build defenses, criminals find new ways in, and the cycle continues. In recent years, something quieter, and frankly more dangerous, has been spreading. It is called synthetic identity fraud.

This is not the same as stealing someone’s name or breaking into their account. Synthetic identities are built piece by piece. A real Social Security number is combined with a name that never existed, or an address that belongs to an empty lot. Over time, these fake people begin to look real. They apply for credit. They open accounts. They pass basic checks.

Eventually, they disappear. The loans go unpaid, the balances are written off, and no individual even knows to complain. That is why regulators find it hard to track and why institutions often realize the problem years later when losses have piled up.

The Blind Spot in Current Defenses

Most fraud tools depend on history. They look at patterns of spending, loan repayments, and account behavior. Synthetic identities thrive at the one stage where history does not exist: the very beginning.

When a new applicant shows up, there is no track record to compare against. On paper, everything looks fine. That clean start is precisely what the fraudster wants. By the time suspicious behavior emerges, the same identity may already be tied to several institutions.

The truth is simple. Prevention has to begin at the first point of contact, not after years of hidden activity.

Profiling: Building Digital Fingerprints

The first step forward is profiling. Imagine building a digital fingerprint for every applicant, not after years of transactions but from the very first moment.

This profile is not just a collection of names, dates of birth, and addresses. It gathers signals such as how often someone applies for credit, how quickly they try again after rejection, what device or browser they use, and whether those details align with trusted sources like credit bureaus.

The value of profiling is that it becomes more effective over time. A genuine customer stays consistent. A synthetic one eventually stumbles and reveals itself.

Comparison: Seeing the Bigger Picture

Profiling on its own is robust, but comparison takes it further.

Picture five applications arriving from different cities. The names and details are not the same. At first glance, they look unrelated. But closer inspection shows three share the same phone number, two use the same device, and all connect through one suspicious email domain. What seemed like isolated requests suddenly became a fraud ring.

Comparison works in three ways. It checks against known fraud profiles. It looks across clusters of related accounts. And it measures new profiles against the behavior of legitimate customers. Once you shift the view from individual cases to connected networks, the picture changes completely.

Graphs, Machine Learning, and the Feedback Loop

To manage this at scale, the system needs the right tools. Graph analytics reveal the hidden links between accounts, showing overlaps that would otherwise remain invisible. Machine learning weighs these connections along with behavioral data and produces a risk score.

Then comes feedback. Every confirmed case of fraud or false alarm feeds back into the system. The models adjust. The accuracy improves. The defenses evolve.

This is not about chasing yesterday’s tricks. It is about staying ready for tomorrow.

Why Business Leaders Should Care

Synthetic identity fraud is not only a technical challenge. It is a strategic issue that touches financial stability, regulation, and customer trust.

The financial impact is severe and often hidden until it is too late. Regulators are beginning to demand stronger controls. Customers lose patience when false positives block them unfairly. Investigation teams face overwhelming workloads when systems are not smart enough to filter cases.

In other words, relying on outdated defenses keeps an institution on the back foot. The cost is measured not only in money but also in reputation and resilience.

From Defense to Resilience

Fraud prevention used to mean building walls. That no longer works; today, resilience matters more than rigidity. 

Synthetic identities are just the start. With artificial intelligence and deepfakes, fraudsters will soon create identities that look flawless. If defenses cannot adapt, they will collapse under pressure.

The better path is to adopt adaptive systems. Profiling, comparison, graph intelligence, and machine learning together make institutions stronger. They protect against fraud. They speed up onboarding for real customers. They reassure regulators that risks are being managed with foresight.

That is resilience. And resilience is a strategy.

The Way Forward

The financial industry faces a clear choice. Continue patching holes with outdated tools or embrace a proactive approach that closes the blind spots. 

This is not only about technology. It is about a mindset shift. Fraud must be seen as a networked economy, not an isolated event. The defenses must be networked as well.

Fraudsters thrive in the shadows. Our job is to turn on the light.

About the Author

Uma Shankar Kulasekaran is a Director of Product Management specializing in financial crime prevention. He is the inventor of a patented approach to synthetic identity detection that integrates profiling, comparison, graph analytics, and adaptive machine learning.