Indian fintechs are embedding AI across their operations to drive credit access (Lendingkart, MoneyTap), strengthen fraud defenses (Razorpay, Paytm, PhonePe), personalize experiences (Policybazaar, Axis Bank), optimize operations (Rakuten’s SixthSense, CRISIL), expand inclusion via voice (NPCI, CoRover, AI4Bharat/Bhashini), and pioneer generative AI and private LLMs (HDFC Bank, InsurStaq.ai, Kroop AI). These deployments have accelerated decisioning, broadened customer bases, and set new standards for innovation and inclusion.
Credit Scoring & Lending
Lendingkart: AI-Driven Credit Scoring for MSMEs
Challenge
Many Micro, Small, and Medium Enterprises (MSMEs) in India lack formal credit histories, creating a significant gap in the availability of financing. Traditional credit scoring systems often fail to assess the creditworthiness of these businesses, leaving them underserved.
Approach
Lendingkart tackled this challenge by creating an AI-driven platform that aggregates over 10,000 data points from sources like business cash flows, tax filings, bank statements, online reviews, and utility payments. By analyzing this data, the AI system generates a comprehensive credit profile for each business, enabling them to access loans despite lacking traditional credit history.
Tech Stack & AI Tools
Lendingkart operates on a microservices architecture using Java Spring Boot, with Python handling data engineering and analytics. Kafka is used for service orchestration, and they are migrating to a cloud-native, serverless setup.
Overall Impact
Increased Approvals: Loans are now accessible for MSMEs that were previously denied.
Faster Disbursals: Loan processing time is reduced from several days to minutes.
Lower Defaults: More accurate credit assessments have led to fewer defaults.
Operational Efficiency: Automation of credit checks has freed up resources.
Key Learnings
Leveraging alternative data can help underserved sectors access credit.
AI and automation scale better than manual systems and drive cost efficiencies.
MoneyTap: Instant Personal Credit Lines
Challenge
Individuals without extensive credit histories often face difficulties obtaining quick personal loans through traditional banking channels, leaving them with limited access to credit when they need it most.
Approach
MoneyTap solved this problem by creating an AI-powered system that uses alternate data sources such as salary deposits, utility bill payments, and digital transaction history to assess creditworthiness. The platform offers instant personal credit lines, accessible via a mobile app, ensuring a seamless user experience.
Tech Stack & AI Tools
MoneyTap’s tech stack includes Java and Python for backend services, Postgres for databases, and Redis for caching. The app’s AI models are developed in Python, likely using scikit-learn and TensorFlow.
Overall Impact
Instant Credit: Users can apply for and receive instant credit lines through a simple mobile interface.
Broader Financial Inclusion: Enabled salaried individuals without credit histories to access personal loans.
Enhanced Customer Experience: The mobile app’s simplicity has increased user engagement.
Key Learnings
Mobile-first AI systems are ideal for democratizing access to credit.
Combining multiple data sources leads to better credit assessments and greater user satisfaction.
Fraud Detection & Risk Management
Razorpay: Real‑Time FraudShield
Challenge
Digital transactions are prime targets for fraud, and businesses face significant revenue loss due to chargebacks and fraud. Detecting fraud in real-time is critical for minimizing financial losses and providing a smooth user experience.
Approach
Razorpay developed FraudShield, an AI-powered fraud detection system that analyzes over 100 variables in real-time to detect fraudulent patterns and anomalies. By monitoring transaction data in real-time, the platform is able to flag and prevent fraudulent activities immediately, ensuring a secure transaction environment.
Tech Stack & AI Tools
Streaming & Storage: Spark Streaming pipelines feeding data into Apache Pinot.
Anomaly Detection: StarTree ThirdEye for real-time anomaly alerts.
ML Models: Supervised classifiers developed in Python, using libraries like scikit-learn and TensorFlow.
Overall Impact
Cost Reduction: Fraud-related losses have been reduced by over 50%.
Faster Detection: Real-time fraud detection allows quicker response and mitigation.
Improved Risk Management: Suspicious activities are flagged in real-time, preventing large-scale fraud.
Key Learnings
Real-time detection and anomaly detection are crucial for preventing financial fraud.
Automated systems are more effective at reducing fraud than manual reviews.
Paytm: Pi - End-to-End Fraud Risk Platform
Challenge
With millions of transactions processed daily, Paytm needed an effective fraud prevention solution that didn’t compromise transaction speed or the user experience.
Approach
Paytm developed Pi, an AI-powered fraud detection platform that processes over 500 million decisions daily. By using a combination of machine learning models and no-code rule engines, Pi is able to detect and block fraudulent transactions while maintaining a fast, seamless experience for legitimate users.
Tech Stack & AI Tools
Pi collects signals from various stages of the customer journey, applies ensemble machine learning models (Python-based), and offers a no-code dashboard for rule adjustments.
Overall Impact
Faster Decisioning: Decision-making time has been cut by 50%.
Centralized Monitoring: One platform now manages all fraud risk, improving operational efficiency.
Scalable Solution: Pi can handle the massive scale of Paytm’s transactions, adapting to growing demand.
Key Learnings
A no-code rule engine can improve the efficiency and scalability of fraud detection systems.
Real-time fraud detection is essential for large-scale platforms with high transaction volumes.
PhonePe: Predictive ML at Scale
Challenge
PhonePe needed a proactive solution to prevent fraud and payment failures without interrupting user experience, especially given the high transaction volume.
Approach
PhonePe employed predictive machine learning models to identify fraud patterns and predict potential payment failures before they occur. The platform continuously analyzes transaction data in real-time, allowing PhonePe to block fraudulent transactions and optimize payment flows.
Tech Stack & AI Tools
Data Processing: Apache Kafka and Spark Streaming for real-time event processing.
ML Models: Python-based models using scikit-learn and TensorFlow.
MLOps: MLflow for model monitoring and management.
Overall Impact
Reduced Failures: Payment failure rates were significantly lowered.
Improved Trust: Enhanced fraud detection has built customer confidence in the platform.
Operational Efficiency: Integrating machine learning models into existing payment systems has minimized disruption.
Key Learnings
Predictive analytics allow platforms to address issues before they occur, improving user trust.
Machine learning and automation streamline operations and scale with user growth.
Personalized Engagement
Personalized Engagement
Policybazaar: AI‑Enhanced Insurance Matching
Challenge
Consumers often struggle to navigate the wide array of insurance options, leading to confusion and abandoned applications. Simplifying this process was crucial for improving conversion rates and user satisfaction.
Approach
Policybazaar leveraged AI and big data analytics to enhance its insurance product matching engine. By analyzing user behavior, preferences, and applying sentiment analysis, the system provides personalised insurance recommendations. It also uses AI to detect potential fraud in applications by identifying discrepancies in submitted data.
Tech Stack & AI Tools
The platform leverages data lakes to store user and claim data, voice analytics, and computer vision pipelines for claim verification, while machine learning models in Python provide personalization.
Overall Impact
Improved Matchmaking: Personalized recommendations led to higher conversion rates.
Reduced Fraud: AI-driven fraud checks ensured the legitimacy of claims.
Increased Customer Engagement: Users were more satisfied with the personalized approach, leading to better retention.
Key Learnings
Personalization powered by AI can dramatically improve user experience and engagement.
Combining AI with big data is an effective way to tackle complex decision-making problems like insurance matching.
Financial Inclusion & Voice Payments
NPCI: Hello UPI – Conversational Voice Payments
Challenge
A significant segment of India’s population, especially rural and elderly users, struggles with using smartphone-based UPI apps due to digital illiteracy, language barriers, and the complexity of text-based interfaces. These users are often excluded from the digital payments ecosystem, limiting financial inclusion efforts.
Approach
To bridge this digital divide, NPCI launched Hello! UPI, a voice-first interface for making UPI payments. The system enables users to initiate and complete transactions through simple voice commands in multiple Indian languages, including Hindi and English. This conversational interface works across platforms—smartphone apps, IVR-based calls, and IoT devices—making it accessible to users with basic phones as well. At its core, Hello! UPI uses a BERT-style natural language understanding engine to interpret user intent, handle regional language inputs, and confirm transactions securely.
Tech Stack & AI Tools
The solution combines a custom speech recognition pipeline using open-source frameworks like Kaldi and PyTorch, with multilingual BERT-based models for intent classification and entity recognition. It’s integrated into UPI 123Pay, ensuring compliance, latency optimization, and secure voice authentication.
Overall Impact
Expanded Access: Enabled over 100 million feature phone users to participate in UPI payments.
User Empowerment: Made digital payments possible for the visually impaired, elderly, and first-time users.
Scalable Rollout: Supported voice payments at scale across Bharat, fostering greater financial inclusion.
Behavioural Shift: Introduced a frictionless user experience that’s driving adoption beyond metros and Tier 1 cities.
Key Learnings
Voice interfaces can dramatically lower entry barriers for tech-novice users.
Multilingual AI solutions are essential for scalable digital inclusion in diverse markets like India.