Supercharge AI and ML adoption for financial institutions — predictive models for credit, fraud and engagement, generative extraction for banking operations, and a cloud-native platform built to scale from pilot to production.
Typical impact across the credit-to-collections lifecycle when financial institutions adopt YantrAI CFS.
Consumer-facing AI/ML models and back-office banking operations, running on a single cloud-native platform.
AI/ML models for customer acquisition, engagement, lending and collections — plus a federated blockchain ledger so multiple organizations can collaborate over a private network.
AI/ML models for Regulatory & Compliance (RegTech), Treasury, InsuranceTech and payments.
Built for public, private or hybrid cloud, with built-in scalability, resilience and agility, and support for both real-time and batch processing.
An engagement model that reduces upfront investment — AI consulting, system integration, and ML model development, deployment, governance and operations — so your team stays focused on the business.
Five areas of depth — from the infrastructure that trains and serves every model to where it shows up in the business.
Every model — credit, fraud, propensity, churn or collections — moves through the same lifecycle: data gathering and preparation, training, evaluation, deployment and governance, so exploring and deploying multiple models stays seamless. Slurm integration handles hardware allocation, monitoring and auditing across every training and inference job.
Credit models trained on bureau, mobile, macroeconomic and alternate data, tuned to the target customer profile. Fraud models combine anomaly detection, behavioral analytics, and network and link analysis with device, IP, location and digital-footprint signals. Models ensemble flexibly into a single score card — more strength and easier interpretation than any one model alone.
Prebuilt and custom models select the right collection strategy and action — the right day, time, channel and message, including the right human agent — to improve both efficiency and customer experience. Deep reinforcement learning models derive personalized collection approaches that maximize long-term outcomes rather than short-term recovery.
Propensity models flag customers likely to buy; uplift models suggest the right intervention to convert them; churn models flag who’s at risk of leaving. In-built and custom propensity, uplift, churn and recommendation models cover every phase of the customer lifecycle.
AI/ML models for Regulatory & Compliance (RegTech), Treasury and InsuranceTech, plus customized text, voice and multimodal generative models for intelligent customer support, insurance claims processing and risk management.
Consumer-banking and banking-operations application, covering the full credit-to-collections lifecycle.
Document extraction and conversational support share the same generative building blocks as every other YantrAI app.
Classical predictive models — credit, fraud, propensity, churn and collections — share the same model-lifecycle tooling as every other line of business.
Bureau, mobile, macroeconomic and alternate data unified on YantrAI’s data federation layer, with an optional private-blockchain ledger for multi-party collaboration.
NVIDIA CUDA and cuDNN for training, NVIDIA TensorRT for inference, with Slurm integration for hardware allocation, monitoring and auditing.
Validated on NVIDIA’s cloud infrastructure — the same GPU acceleration and model-lifecycle tooling behind every other YantrAI deployment, not a bespoke build.
From credit scoring to collections — see what a cloud-native, GPU-accelerated platform can do with your own data.