Customer risk assessment has been a significant process in financial services ever since to include customers in KYC and AML regulations. Conventional practice required manual checks, constraints, and time consuming onboarding. However, today fintech companies are presenting the landscape in a new light, bringing in smarter, faster and more scalable ways of evaluating customer risks.

This article addresses the ways in which fintechs are changing customer risk evaluation with digital innovations, automation, artificial intelligence and real time analytics.

Table of Contents show

The traditional challenges of customer risk assessment.

For decades, financial institutions had been using manual checklist-orientated ways of gauging customer risk. Analysts would review documents and carry out credit histories and run basic watchlist checks thus to classify customers as low, medium or high risk. These approaches however proved quite useful at the time but presented with a number of challenges;

  • Time-consuming processes
  • High operational costs
  • Inconsistent decision-making
  • Limited scalability
  • Difficulty detecting sophisticated fraud patterns

Furthermore, with the rapid growth of transactions in the digital space and with expectations from regulators rising, the old approach can no longer serve the purposes.

Fintechs: Driving Innovation in Risk Assessment

Fintechs are uniquely prepared to disrupt the legacy systems with their emphasis on technology first solutions. They utilize automation, artificial intelligence (AI), machine learning (ML), big data and cloud computing to implement modernization in customer risk assessment. Here’s how they’re doing it:

Automated Onboarding and Identity Verification

Fintech platforms utilize eKYC tools to automate onboarding process. Customers are able to verify their accounts by uploading their selfies and government issued ID within seconds with help of facial recognition and Optical character recognition (OCR).

This automation not only enhances the customer experience but rather allow for continuous and smooth risk scoring at the beginning of the customer journey.

Also know: The Role of Liveness Detection in the Fight Against Deepfakes

Advanced Analytics and Machine Learning

Fintechs use models of machine learning that become smarter in time. These models train on large datasets in order to identify the pattern that may suggest fraud, money laundering or identity theft. For instance, if someone in the system, suddenly, begins to do high-value international transactions outside what is ordinarily done, then the system flags such and requires further attention.

Machine learning also assists in minimising the false positives, which is an integral obstacle in the classic models of the risk, by reviewing contextual information to reach better conclusions.

Behavioral Biometrics

Some of the fintechs are already using behavioral biometrics, which is the technology used to measure how a person operates with his/her device, for example, typing speed, swipe patterns or mouse movements, etc. These indicate subtle warnings to pick potential identity fraud & understand current customer risk without disturbing the user experience.

For example, should a customer’s typing pattern suddenly change during a transaction the system might flag the session for verification.

Integration with Global Watchlists and Databases

Present day fintech risk engines track customers to global watchlists, PEP database as well as sanctions lists in real-time data sources. Such integrations ensure that compliance checks occur all the time and they are in compliance with local and international laws and regulations.

Unlike legacy system that also require manual updates, a lot of fintech platforms utilize APIs to retrieve live-compliance data which would mean better accuracy and less regulatory risk.

Customized Risk Profiles Depending on Business Type

Fintechs customize risk assessments according to customers profiles and industry segments. A small online retailer will have a completely different risk profile than a cryptocurrency investor. Adapting to a risk-based approach, with the level of the due diligence changing depending on the customer’s individual risk level, is enabled by customisation.

This degree of flexibility enables fintechs to meet AML direction such as the EU’s 6AMLD or the FATF’s recommendation that put emphasis on due diligence that is contextual and proportional.

Scalability and Global Reach

Conventional models of risk evaluation have difficulty with cross-boundary compliance. While sclability is often a part of the why fintechs build their solutions. Cloud Based Platforms, Multi-Language Support and Jurisdiction Aware Compliance tools enable them to measure risk correctly in different geographical terrains.

Specifically, this scalability is particularly relevant for the global fintechs, who serve customers in geographies where the regulatory requirements vary.

Also Know: Top 10 Crypto-Friendly Banks

The Advantages for Fintechs and for Their Customers

A modernization of customer risk assessment brings tangible benefits:

  • Faster onboarding and user satisfaction
  • Lower compliance costs through automation
  • Better detection of frauds and lesser financial crime.
  • Greater auditability, reporting for regulators.
  • Scalable compliance as the business scales up

Customers get smoother dealings, and fintechs stay compliant, a necessary dichotomy in today’s fast-paced financial generation.

Final Thoughts

As fintechs grow, their customer risk assessment strategy will still be the foundation of secure and compliant as well as customer-friendly financial services. By leveraging real-time data, AI, automation, and behavioral analytics, fintech companies are over-riding the old perception of understanding risk and managing it. The digital financial sphere will be made safer for everyone as old age is now making way for agile, intelligent systems that learn, adapt and protect.