Customer Risk Rating in Banks vs Fintechs: What’s the Difference?
Both traditional banks and fintech companies are under growing pressure in the modern-day financial environment, as they should be more accurate in their assessment of customer risk. Whether it’s for regulatory compliance, fraud prevention, or operational efficiency, Customer Risk Rating (CRR) plays a pivotal role in how financial institutions manage relationships. However, the attitude to CRR can be very different for banks and fintechs because of the differences in legacy systems, regulatory supervision, agility, and customer expectations. This article discusses the main differences in the way banks and fintechs treat customer risk rating and the consequences of each of the approaches.
Understanding Customer Risk Rating
Customer Risk Rating refers to the process of assessing the risk of a customer in regard to various factors such as identity, geographic location, transaction behavior, occupation, and source of funds, among others. The goal is to categorize customers (low, medium, or high risk) to determine the level of due diligence required—whether it’s Simplified Due Diligence (SDD), Customer Due Diligence (CDD), or Enhanced Due Diligence (EDD).
Although the main idea is the same throughout the financial sectors, it is possible to implement CRR differently based on the institution’s structure, technology stack, and its compliance requirements.
Regulatory Landscape: Banks vs Fintechs
Traditional Banks
Banks are some of the most regulated institutions in the world. They must adhere to stringent KYC processes laid out by central banks, financial intelligence units, and international standards such as FATF (Financial Action Task Force). This has led to their risk rating processes being more rigid, audit-proof, and policy-driven.
Fintechs
Fintechs, and especially the ones that provide payment services, digital wallets, or lending, are regulated too, but with a lighter or more flexible framework of compliance, depending on the jurisdiction and type of license. This would enable many fintechs to create more agile and tech-oriented CRR systems that are speed-optimized and scalable.
Technology and Data Usage
Banks
Legacy systems or fragmented platforms are what most banks use for compliance operations. This reduces their capability of collecting and processing real-time data effectively. A lot of banks still use rule-based engines that apply risk scores based on static data and minimal machine learning or AI prowess.
Fintechs
Fintech companies are mostly “born digital”. They use sophisticated technologies like AI, machine learning, and big data analytics to assign dynamic risk scores that are based on behavior. For example, a fintech can change the risk profile of a customer in real-time as per the spending habits or change in geo-location. Fintechs also tend to add APIs and third-party KYC/AML services to fetch a larger set of customer data, resulting in more granular and dynamic risk evaluation.
Customer Onboarding Experience
Banks
Because of the tight internal processes and regulatory oversight, banks tend to have a cumbersome and paperwork-intensive onboarding process. Customers may have to deal with long forms, in-person authentication, and a long queue for the approval process-especially for medium or high-risk customers.
Fintechs
Fintechs provide an experience that is much more seamless. Their digital platforms usually come with automated identity authentication, real-time risk scoring, and instant approvals. The customer risk rating is usually integrated smoothly in the background, and this provides a frictionless experience, all while following the norms of compliance.
Flexibility and Adaptability
Banks
Banks are normally less flexible when it comes to the adjustment of CRR frameworks. The updating of risk models may require time-consuming approval cycles, retraining of staff, and changing outdated infrastructure. This could be a weakness, especially in an age where financial crime is a dynamic thing.
Fintechs
Due to leaner teams and modern infrastructure, fintechs can easily change their CRR models. For example, if new risk indicators (such as emerging fraud typologies) are identified, fintechs can integrate them into their systems within days, if not hours.
Risk Appetite and Customer Segmentation
Banks
Conventional banks are conservative in their appetite for risk. They may avoid onboarding customers from high-risk countries or politically exposed persons (PEPs), regardless of potential profitability. Their CRR models are usually tailored in a way that they focus on minimizing institutional risk more than anything else.
Fintechs
Fintechs, and in particular startups or niche players, are more likely to serve higher-risk segments of customers, such as gig workers or digital nomads, or unserved populations. Their risk rating systems are, therefore, not only for compliance purposes but also to open up business opportunities with the risk mitigation strategies in place.
Human vs Automated Decision-Making
Banks
In the case of banks, customer risk assessments usually include manual reviews by the compliance officers for high-risk or complicated cases. This human-oriented approach entails close examination but slows down the process and escalates the cost of operation.
Fintechs
Fintechs rely heavily on automation. AI-based CRR systems can make instant decisions with a high rate of accuracy using large volumes of data. However, they still have the mechanisms of human-in-the-loop for exception handling and quality control.
Conclusion
Customer Risk Rating is an integral component of financial compliance and management of risks. While the traditional banks depend on outdated, compliance-heavy frameworks, fintechs enjoy speed, flexibility, and automation. The awareness of these differences can enable businesses, regulators, and consumers to understand the strengths and weaknesses of each approach. Perhaps, the future is in a hybrid model – the robustness of banking systems combined with fintech’s innovation – to provide secure and seamless financial services.
