Online lending has opened the door to faster, broader access to credit, but that same speed and accessibility can be attractive to bad actors that are looking to exploit the system. Unlike traditional banking, where you might meet a customer in person or have years of account history to rely on, online lenders often have to make quick decisions based on limited and sometimes questionable data. That creates the perfect environment for identity fraud, synthetic applications, and money laundering schemes to thrive. 

Why Online Lending Carries Higher Risk 

When everything happens online, we remove the human factor and judgement. Fraudsters know this. They take advantage of automated onboarding, especially when companies implement weak document checks, and underdeveloped monitoring systems. 

The most common risks include: 

  • Synthetic identities: Fraudsters combine real and fake data to create a new identity that looks legitimate. They apply for loans, take the money and disappear. 
  • Account takeovers: Stolen credentials are used to access or apply for loans in someone else’s name. Lending platforms end up chasing people who did not make a loan. 
  • Money laundering: Money launderers apply for loans and repay them with illegally-derived funds. The loans are often created with one sole purpose – to launder funds. 

Where the Risk Tends to Hide 

In online lending, the biggest risks don’t always stand out. They’re often buried in applications that seem polished and legitimate at first glance. That’s because fraudsters know how to play the system. They often use real-looking documents and mimic typical borrower behavior. You can only spot inconsistencies if you look deeper. 

  • Applicants from high-risk jurisdictions
    Someone may submit all the right paperwork, but if the IP address, phone number, or employment data links back to a sanctioned or high-risk country, that’s an immediate concern. These indicators don’t always get flagged if the system focuses only on document validation. 
  • Applications with ‘too-perfect’ documents but no digital trail
    Fraudsters using synthetic identities often submit ID cards or documents that look professionally made and error-free, because they are. But when you look beyond the surface, there’s no real person behind them. No credit history, no digital footprint, no trace of them outside of what they’ve submitted. A genuine person applying for credit typically has some online presence; a LinkedIn profile, prior credit activity, a long-used phone number. If none of that exists, it’s a sign that the identity may not be real. 
  • Loan stacking across platforms
    Fraudsters often apply for multiple loans at the same time on different platforms, knowing there’s no shared alert system. The activity looks normal in isolation; but viewed in context, it reveals a pattern. This is particularly dangerous for lenders without access to external data sources or consortium alerts. 
  • Applying for multiple loans in a short timeframe
    Fraudsters will often submit several loan applications across different online lenders within hours or days. Because many platforms don’t share data in real time, this activity can go unnoticed. Each application might look normal on its own, but together they form a clear pattern of loan stacking – where the goal is to withdraw as much money as possible before defaults start hitting. Lenders without access to credit bureaus or shared fraud intelligence are especially vulnerable to this tactic. 

Practical Ways to Spot High-Risk Customers 

Here’s where compliance professionals can make a real impact – by putting strong detection tools and processes in place before bad actors slip through. 

  1. Look beyond documents: Use multi-layered checks. Don’t rely solely on ID documents. Add biometric checks, liveness detection, and device intelligence. If someone has a credit history that looks perfect but is logging in from a device known to be linked to fraud, that should raise a red flag. 
  2. Screen against relevant watchlists: Sanctions, Politically Exposed Persons (PEPs), and adverse media databases should be integrated into your onboarding flow. This helps you catch known bad actors or suspicious connections before they’re approved. 
  3. Risk-rate customers at onboarding: Assign a risk score based on factors like geography, document quality, employment type, and transaction history (if available). Use that score to guide the level of due diligence needed – don’t treat every application the same. 
  4. Use behavioral analytics: Track how users interact with your site or app. Do they complete the application too quickly? Are they using a VPN or suspicious IP address? Are there signs of automated input? These subtle indicators can flag high-risk behavior. 
  5. Watch for transaction red flags: Set up rules for out-of-pattern behavior. Is a borrower repaying much faster than expected? Are they using third-party bank accounts for repayment? These behaviors might look positive on the surface, but they can mask laundering activity. 
  6. Have clear escalation paths: When something looks off, your frontline systems should route it for manual review – ideally to someone trained to ask the right questions and dig deeper. Avoid an “approve all” mindset. 

Final Thoughts 

Identifying high-risk customers in online lending isn’t about finding a perfect system; it’s about building enough checks and context into your process to catch the things that don’t feel right. The goal isn’t to block every unusual applicant, but to recognise when something doesn’t fit and take a closer look before it turns into a problem.