Why more data isn’t always the answer – but a more holistic approach is.
The Growing Threat of Application Fraud
The world continues to become more and more digital – and fraudsters are taking advantage by consistently finding new ways to exploit any weaknesses in technology and financial services systems. Application fraud in particular has emerged as a significant threat in financial services, with attempts (and the various types) increasing steadily. According to TransUnion’s 2023 State of Omnichannel Fraud Report, nearly 5% of digital transactions globally in 2022 were found to be possibly fraudulent (4.2% for financial services specifically), and there were over $4.5 billion in outstanding balances in the U.S. for auto loans, credit/retail cards, and unsecured personal loans, thanks to synthetic identities (which incidentally marks a 27% increase since 2020, and the highest level ever recorded). Additionally there was an increase of 39% from 2019-2022 in cases of fraud attempts in financial services, with the top type being identity fraud.
So what does this mean for financial institutions, payment providers, lenders, fintechs, etc.? It means that as fraudsters and their methods evolve, so too must the ways in which we as an industry detect and prevent it. But how? One key is data orchestration. Because with a more holistic, comprehensive view of your customers you can:
- More accurately detect and prevent fraud, at onboarding and beyond, and;
- Ensure that genuine, creditworthy customers don’t feel the pain while you do so
Fraud Attempts on the Rise
Fraud attempts are increasing. Rapidly. Which makes it more imperative than ever that the financial services industry gets prevention right. According to TransUnion, these are the top fraud types and their growth this year:
|Fraud Type||Percent of Digital Fraud in 2022||Volume Change 2019-2022|
|True Identity Theft||6.2%||81%|
To prevent application fraud, financial services institutions must use various detection mechanisms, typically curated from data partners/sources, including identity verification, screening, and scoring. Identity verification involves verifying that the applicant is who they claim to be, while screening involves checking the applicant’s information against various databases, including credit bureaus and watchlists, to identify red flags. Scoring involves assessing the risk associated with the applicant based on various data points, including credit history, employment, and financial data. Looking at various data sources, including open banking, bureau data, email and social media, device information, KYC, and sanction screening can all be used to check whether a) a person is legitimately who they claim to be and b) whether they really intend to actually use the financial product in a responsible way (i.e. will they pay you back??).
More Data To Combat Fraud? Or BETTER Data?
So it’s clear that fraud prevention is critical. But if your immediate reaction is to buy all the data… think again.
From TransUnion again, “the knee-jerk response to rising data breaches and persistent digital fraud might be to increase identity verification and authentication checks. However, the transition to an always-on, digital-first customer experience, evidenced by the dramatic increase in digital transactions over the past few years, means fraud leaders must be aware of customer experience and enable the business to drive top-line growth while reducing fraud risk.”
So despite how tempting it is to just use more and more data, you need to balance that with a) the consumer experience (are you ready to add more friction to the journey?) and b) the unnecessary cost and inefficiency of buying more data than you need. Because the better you get at accessing and integrating the right fraud data, at the right time in the customer journey, the better results you’ll see:
- Less friction in the consumer experience
- More accurate fraud risk models
- Increased ability to assess fraudulent activity and the intent to pay
- More growth – because ultimately, the more adept you get at preventing fraud, the more confident you can be in your decisions, enabling sustainable business improvements across the customer lifecycle
Sidenote: Predictive analytics, like embedded machine learning and artificial intelligence, also helps, by automatically analyzing vast amounts of data and offering insights into patterns of behavior that may indicate fraud.
Eliminate Decisioning Silos
Traditional fraud detection methods often result in siloed environments between fraud and risk teams, leading to an incomplete view of the customer and their creditworthiness. To overcome this challenge, financial institutions need to think about adopting a holistic, end-to-end risk decisioning solution that integrates fraud and risk management. This approach enables a more comprehensive view of your customers and their creditworthiness while accurately detecting fraud by eliminating the siloed environment between your fraud and risk teams.
A more holistic, integrated view of your customers enables you to stay ahead of threats, and an end-to-end risk decisioning platform ensures you can continually improve your fraud risk models and optimize decisions as threats evolve – all right alongside your credit risk decisions. Eliminating these siloed environments offers maximum flexibility and agility at every step of your risk decisioning processes. Reduce the complexity of managing multiple online fraud detection tools and disparate decisioning systems with one unified, end-to-end solution for fraud, credit, and compliance across the customer journey. And watch your business grow as a result.