Fig Financial’s AI Shield: A New Digital Guardian for Vulnerable Seniors

TORONTO, ON – June 15, 2026 – On World Elder Abuse Awareness Day, the grim statistics surrounding financial fraud serve as a stark reminder of a growing societal crisis. The Canadian Anti-Fraud Centre reported that Canadians lost over $704 million to scams in 2025, a figure that experts agree represents merely the tip of the iceberg, with estimates suggesting only 5-10% of such crimes are ever reported. Within this shadow economy of deceit, older adults are disproportionately targeted and suffer the largest losses.

In this high-stakes environment, Fig Financial, a Toronto-based fintech backed by the Ontario Teachers’ Pension Plan, is moving beyond traditional, reactive fraud alerts. The online lender has developed an in-house artificial intelligence tool, dubbed Fig Shield, designed not just to detect fraud, but to identify potential victims before they are financially devastated. It’s a strategic pivot that highlights a crucial question for the entire financial services industry: where does the line between compliance and a deeper duty of care lie?

A New Frontline in Fraud Prevention

At its core, Fig Shield is a proprietary machine learning model that analyzes behavioral data from loan applications. Rather than simply flagging suspicious transactions, it looks for patterns that place an applicant within a known “scam cluster”—a set of behaviors and characteristics typical of individuals being actively manipulated by a fraudster. This could include signs of coaching, unusual urgency, or other subtle indicators that a human reviewer might miss in a sea of data.

According to Fig, these high-risk clusters represent as little as 8% of daily applications, yet they are the source of a significant portion of fraud-related losses. By automating the identification of these applicants, the AI allows the company's fraud team to bypass hours of manual review and intervene directly and proactively.

“The heartbreak of these scams is that victims rarely know they're being exploited until it's too late, particularly vulnerable older adults,” said Ardalan Shojaei, General Manager of Lending at Fig Financial. “With Fig Shield, we can spot behavioural patterns of victims who have fallen prey to scams. This lets our fraud team proactively reach out to applicants, supporting to stop a bad situation in its tracks and fundamentally changing how fast we can protect people.”

This approach isn't just about altruism; it's smart business. By preventing fraudulent loans from being issued, the company mitigates direct financial losses. Furthermore, in a market where trust is a primary currency, positioning itself as a guardian of customer assets builds significant brand equity. While many financial institutions use AI for fraud detection—often through third-party platforms like Flagright, which Fig also uses for anti-money laundering (AML) compliance—the development of a specialized, victim-centric tool like Fig Shield indicates a deeper, more integrated strategy. It signals a move from a defensive posture to an offensive one in the war against financial crime.

The Anatomy of a Modern Scam

The need for such tools is underscored by the sheer scale and sophistication of modern fraud networks. These are not isolated incidents but organized criminal enterprises that leverage technology and psychology with chilling efficiency. Fig's unique position as a loan originator provides a stark window into their methodology. The company reports that approximately 66% of all scam cases it identifies involve borrowers aged 65 or older. More alarmingly, their data shows Baby Boomer applicants are about 75 times more likely to be targeted as scam victims than their Gen Z counterparts.

The data also paints a detailed geographic and demographic picture of vulnerability. The highest rates of scams are found in Saskatchewan, Manitoba, and Alberta, at 1.5 times the national average. Despite this, nearly nine in ten identified cases originate from urban centers, and victims are most concentrated in the modest $40,000-$50,000 income bracket—a demographic that can least afford to absorb such a financial shock.

Law enforcement officials confirm that once the money is gone, the chances of recovery are slim to none. “The reality is that most of the money lost to these scams is never recovered, and seniors are disproportionately the targets,” says David Coffey, a Detective with the Financial Crimes Unit at the Toronto Police Service. He notes that the fraud networks are “sophisticated, persistent, and increasingly hard to detect.”

For this reason, prevention at the point of transaction is paramount. “When financial institutions can identify a potential victim at the point of application and intervene early, that's one of the most effective tools we have for prevention,” Coffey adds. This makes initiatives like Fig Shield not just a corporate project, but a critical piece of the public safety infrastructure.

Beyond Compliance: The Evolving Responsibility of Financial Institutions

Fig's proactive stance raises broader questions about the role of financial institutions in the 21st century. For decades, the primary responsibility was to comply with regulations and react to reported fraud. However, the use of predictive AI to monitor customer behavior for signs of victimization marks a significant ethical and strategic evolution.

This approach is not without complexity. The use of AI for behavioral monitoring walks a fine line between protection and privacy. As institutions collect and analyze ever-more-granular data, questions around consent, transparency, and algorithmic bias become critical. For instance, if a model is trained on data showing that most victims are urban and fall into a specific income bracket, it risks creating a system that either over-scrutinizes or overlooks individuals who don't fit the profile. Guidance from bodies like the Office of the Privacy Commissioner of Canada emphasizes fairness and accountability in AI deployment, a challenge that all institutions in this space must navigate.

Nonetheless, the trend is clear. Major Canadian banks are also investing billions in AI and machine learning, albeit often with less public-facing detail about specific programs. Fig Financial’s move to publicly champion a tool like Fig Shield may force a broader industry conversation about setting a new standard of care. In a competitive landscape, demonstrating a tangible commitment to protecting the most vulnerable clients could become a powerful differentiator, transforming a moral imperative into a measurable bottom-line advantage.