Lending Redefined: Implementing ML To Enhance Products, Workflow, and Customer Experience

An AI-First-Approach is a core pillar of fast-growing fintech companies, aimed at enhancing both internal processes and product efficiency.

Let ML Decide: The Credit Scoring Evolution 

Automation in lending and scoring has existed since the mid-20th century, but when we talk about digital lending today, we no longer refer to traditional rule-based scoring with questions like “Do you have a higher education?” or “Are you married?” – we are talking about a comprehensive decision tree and its reliable automation.

The shift towards analyzing increasingly large volumes of data is driven by a fundamental difference in business models. Traditional banks typically provide customers with a few large loans in their lifetime (such as mortgages or car loans), while fintech companies focus on small amounts that clients can receive monthly.

Accordingly, the approach to lending and risk management differs. In fintech, the depth of the decision tree can reach thousands of nodes. To automate this process, you can develop proprietary ML algorithms based on models like Gradient Boosting Machines.

These ML methods are particularly effective for estimating each client's lifetime value and evaluating the potential for long-term cooperation. This allows you to balance desirable conditions for customers, attract new users, and maintain strong profitability.

Behind Limit Strategies

ML also helps fintech companies answer a critical question once a client is approved: How much money can be lent? It might be helpful to try ML-driven limit strategies inspired by Recommendation Systems, models typically used in products that adapt to user behaviour and preferences. Almost everyone has experienced such models in action: the content suggested to you on TikTok or YouTube is powered by them. 

The comprehensive strategies can allow you to assess the business impact on credit risk (the risk of a client not repaying a loan) and the risk of under-receiving profit (i.e., missed opportunity from issuing loans with lower limits or declining them entirely).

 See, Verify, Confirm

Deep learning–based verification technologies have gained traction across the lending industry to strengthen fraud prevention and streamline identity verification.

One of the helpful solutions here focuses on verifying the authenticity of a user’s face during onboarding or transaction approval processes. It combines face detection with liveness verification to confirm that the input comes from a live person, not a static image or recording. It helps to cross-check each new face against an existing database. With that, a system identifies individuals attempting to submit multiple applications under different names, which is a common tactic in fraud scenarios.

Controlling the Quality of 100% of Phone Calls 

Generative AI models like LLMs are now transforming internal operations, especially in customer support and call centers. Previously, supervisors in large teams could manually review only a fraction of calls (often less than 10%), limiting the ability to ensure consistent quality.

Now, the AI tools can improve call centers' productivity:

• Transcribe 100% of incoming and outgoing calls in the local language and translate the transcripts into English.

• Assess call quality based on predefined criteria, such as detecting deviations and incorrect responses compared with the original call script.

• Generate individual reports to support supervisor feedback and employee training, and management reports to monitor the call center’s effectiveness and overall performance.

This tool is perfect for scaling a business’s quality standards across multiple countries. It allows for transparent, data-driven assessments of employee performance and offers insights for the leadership team on user problems and service issues.

Machine learning and artificial intelligence should be implemented into scoring, fraud detection, and customer service to enhance operational efficiency, personalize customer experiences, and ensure consistent quality. 

However, it is essential to remember that all such scenarios are handled in strict compliance with relevant data protection laws, including the GDPR. Constraints, legal, ethical, and technical, are not just boundaries, but a framework for responsible innovation.