Revolutionizing a FinTech Platform with AI-Driven Solutions

DataPro worked with the client across multiple teams to embed AI-powered solutions into their platform. Rather than focus on a single feature, we helped build an ecosystem of AI services that powered:

• Faster, automated user onboarding

   

• Real-time KYC document validation

   

• Proactive fraud risk scoring

   

• Smart support automation

   

• Embedded analytics and recommendations for users

   

Let’s break it down.

1. AI-Powered Onboarding and KYC Automation

The original onboarding process required manual review of user-submitted documents. This slowed down approvals and created bottlenecks in growth.

We built an OCR + NLP pipeline to automate:

• Identity document scanning and validation (passports, national IDs)

   

• Name and DOB extraction

   

• Selfie-to-ID photo face matching using deep learning

   

• Real-time sanctions and PEP list checks

   

• Country-specific document formatting rules

   

✅ Result: Reduced manual KYC processing time by 90% and boosted onboarding conversion by 28%.

2. Predictive Fraud Detection and Risk Scoring

Using the client’s transaction logs, account histories, and device metadata, we developed a fraud detection model that used:

• Behavioral analytics to track how users interacted with the app

   

• Graph-based models to flag linked accounts and money laundering risk

   

• Supervised ML trained on previous fraud cases

   

The fraud engine generated a real-time risk score per transaction, which was used to:

• Auto-approve safe actions

   

• Trigger manual review for risky ones

   

• Dynamically adjust verification steps

   

✅ Result: Detected 95% of suspicious behavior early, preventing major fraud events.

3. Embedded AI Insights for Customers

To stand out in a crowded neobank space, the client wanted to offer personalized, data-driven insights to users.

We built a recommendation engine that analyzed:

• Spending patterns

   

• Bill payment behavior

   

• Peer group comparisons

   

• Savings and cash flow trends

   

The engine provided:

• Weekly financial summaries

   

• Personalized tips (e.g., “you spent 30% more on dining this month”)

   

• Smart nudges (“transfer extra funds to savings before rent is due”)

   

✅ Result: Increased in-app engagement by 31% and improved user retention by 19%.

4. Automated Support Triage with AI Chat

Support was overwhelmed with common issues, password resets, balance checks, identity questions, etc.

We developed a hybrid AI chatbot + live agent system:

• The chatbot handled 70% of user queries instantly, using intent classification and FAQ generation

   

• More complex cases were routed with full context to live agents

   

• Agents received suggested replies, speeding up response time

   

The bot improved over time by learning from resolved tickets and agent interactions.

✅ Result: Cut average resolution time from 18 hours to under 4 hours.

5. AI for Regulatory Compliance and Audit Trails

We used NLP to scan:

• User-uploaded documents

   

• System logs

   

• Internal support transcripts

   

The system could flag:

• Terms violating regulatory guidelines

   

• Gaps in data collection

   

• Risky financial patterns (e.g., possible AML violations)

   

This provided the compliance team with audit-ready reports and alerts to intervene proactively.

✅ Result: Enabled faster, more confident compliance sign-offs, especially during quarterly reviews.

6. Scalable AI Infrastructure Integration

We didn’t just build models, we integrated them into the platform seamlessly:

• Deployed models via containerized microservices with REST/gRPC APIs

   

• Used feature stores and data versioning for consistent retraining

   

• Added observability tools for model drift and latency tracking

   

• Ensured everything worked within the platform’s real-time, high-availability environment

   

✅ Result: All AI components ran with <100ms latency and 99.99% uptime.