We help Banks Improve ROA

Our solution based on Classical Machine Learning + Quantum Optimization helps Financial Institutions improve their Return on Assets.

What We Do?

We help Financial Institutions improve their Return on Assets (ROA) through the use of Machine Learning based Credit Decisioning and Quantum based Asset Liability Optimization under a Hybrid Model.


Credit Decisioning

Our machine learning platform combines experimental data with multiple data elements provided by clients to deliver predictive analytics for real-time decision making. The platform uses proprietary machine learning algorithms to find anomalies and patterns based on historical and real-time data for decisions on loans, mortgages, trade credit, etc.


Quantum Optimization

We solve multi-period stochastic optimization problems using a Hybrid Classical+Quantum model. Our algorithms leverage the power of Quantum Computing to derive optimized samples of potential optimal solutions for Asset Liability Management to help Financial Institutions improve their Return on Assets (ROA) on their Banking Book.


Predictive Machine Learning

Our hybrid solutions can be leveraged to support predictive analytics and mapping outcomes based on assumptions.

Why Choose us?

Innovative Hybrid Solutions that combine Classical approaches with the power of Quantum.

Research based Solutions

Our solutions are based on over 1400 Classical experiments and numerous Quantum experiments.

Easy to Implement

Simple API’s and Simulation Engine so you can test.

Customized Experience

We understand that one size does not fit all. We work with you to customize our solution to help you meet your objectives.

Latest Posts

Read about Machine Learning and Quantum Advances, tools and more.

What's in the name?
49 days ago

DWave announces 5000Q Advantage processor

Congrats to DWave on the announcement of their next generation 5000Q Advantage processor. At CogniFrame we are delighted to closely collaborate with DWave to build and deploy quantum applications to solve intractable and computationally challenging problems for financial institutions. CogniFrame# DWave# Quantum# Qubits2019#

By Vish R.

Adaptive vs Static
231 days ago

The Business Case for Classical Machine Learning in Credit Decisioning

About two years ago, I had a meeting with the Chief Risk Officer of a large sub prime lender in Toronto. Business was booming, delinquencies were high but contained within a range and the CRO was disinterested in using Machine Learning for Credit Decisioning. He felt that his algorithms were time tested, adaptive in his mind didn’t mean much and he just saw this as incremental costs for his business. He had built a large army of thirty people just for underwriting and didn’t see why he needed to change the approach. We just agreed to disagree.

By Vish R.

Pre-approved vs Real Approved
231 days ago

Can we truly get to paperless credit decisioning?

You are pre-approved in 5 minutes says the advert by a lender. Now what does that mean. Nothing much it seems. All it really says is you meet some minimum criteria, but we cannot take you at your word, so we need to see documentary evidence and pull a credit score. Two days or more and a day or two to receive the money in your account. This does not sound like progress. Back to the future!

By Vish R.