Design, develop, and deploy powerful and flexible solutions
Pivotchain makes machine learning accessible to our customers. Our solutions are designed for every platform (cloud/on-premise) with self learning & interactive dashboard controls making it easier for our customers to create and maintain models.
Chatbots on IBM Watson based cognitive computing framework for every use like customer onboarding (KYC & AML), tracking everyday payments, transactions & balance updates, fraud remediation, bill-pay, policy changes, real-time inventory check, distribution status etc.
Use of 5000+ features from Credit bureau, Loan portfolio performance, Demography & Social media to develop credit underwriting model built on both statistical approach and Machine learning approach. Deployment ready solutions for cloud or on-premise for large scale loan and wallet limits approval.
Built Credit, Risk, Affluence and other scorecards based on Mobile Wallet transactions, SMS transactions & internal portfolio history. These models are integrated in a mobile lending app for underwriting unsecured and secured loans.
Predicting potential defaulters among a large, geographically spread loan portfolio for Centralized collections team (comprising of Home, Vehicle, Education loans among others).Based on the information – Consumer profile, transactional data and collection flow rates at various points of collections cycle (30dpd, 60dpd), delinquency prediction scorecard for “current portfolio” and payment prediction scorecard for “90+ days past due” portfolio was developed and deployed.
Propensity modeling, identifying the right match between products and customers based on customer preferences, needs and attitudes leading to personalized recommendation of products, services, pricing, marketing, sales and communication to customer needs.
Implementation of a comprehensive program to detect and prevent Credit Card fraud that can lead to a faster and more effective detection, higher visibility of exposure across channels, improved customer retention, lower total cost of ownership, protection of reputation and reduced financial losses. Augmenting traditional fraud systems through Advanced analytics engine for outlier detection, linkage analysis & fraud-scoring improves fraudulent transaction detection.
Machine learning based models for customer value and churn prediction to help identify high-risk “real” churn targets for tailored treatments (e.g., special retention campaigns, VIP service, liberal fee-reversal policies) and eliminate the “other” churners such as bad payers.
ML models to identify and validate campaign ROI/effectiveness Accurately measure and report different parameters to enrich insights and decision making. Tracking and reporting for online to online and offline to online interactions Setting up dashboards and custom report.
Segmenting customers across multiple dimensions (income, risk, loyalty, service and propensity) to target the consumers & retailers with relevant offers. Customer segmentation can be based on various features from geographic, demographic, psychographic & behavioral domains.
ML Models to Improve fill rates and deliver growth, Eliminate wastages and improve margins, Conduct root cause analysis for excess inventory or stock outs, Optimize freight utilization by improving loading levels and routing, reducing cost to serve and improving service levels, Risk analysis for network failure by identifying all possible scenarios and predicting losses.
Entity extraction from unstructured data (images, videos, calls and raw text) and classifying them based on different taxonomy. These data points help build strong customer identity across all source and format of data. We work with multiple institutions to extract and analyze data from utility Bills, ID documents, Cheques, bank statements, payslips and other documents to understand the customer better.
Helps in improving asset productivity by using data to anticipate machine breakdown. Machine-learning techniques examine the relationship between a data record and the labeled output (e.g., failures) and then create a data-driven model to predict those outcomes. The model recognizes patterns from historical events and either predict future failures or prevent them based on learnings from specific breakdown root causes.
Machine learning models to reduce perceived demand variability by capturing and modeling the attributes that actually shape demand while filtering out the “noise”— random and unpredictable demand fluctuations. Different Models can be deployed for different scenarios like demand estimation when the time horizon is short (future prediction based on past time-series data), treatment of seasonal demand with trend, product importance with respect to demand & sales/supply etc.
Optimization is a major challenge in every industry. Machine learning models to optimize complex process in real time - determine where to dedicate resources to reduce bottlenecks and cycle time minimizing losses.
ML models for Identification of root causes for low product yield e.g. determining root causes for quality issues developed during or outside of manufacturing (during delivery, in supply chain etc.)