Simplify ALM

We are on a mission to build the first, open, AI-powered, fully digital loan assets and excess deposit syndication network with continuous rebalancing among its member lenders.

61% of banks[1] and credit unions have over 40% of their loan assets concentrated in a single loan type.

This concentration, combined with most often geographic concentration, sets elevated and more sensitive credit risk for this sector of the lenders. Continuous loan syndication network offers an opportunity to rebalance the loan assets for a more appropriate mix.

Based on our experience working with the executive teams of community banks and credit unions, Asset Liability Management is, most often, seen as a regulatory burden rather than a powerful tool for managing financial institution performance and stability.

Our Vision

The recent exponential advances in Machine Learning, Artificial Intelligence in whole, abundance of cheap compute power, data storage and wide democratization of access to broad range of key economic condition historical metrics and asset( as well as investment)  trading capabilities - all opens up an opportunity to deliver the next generation ALM with continuous rebalancing of assets and excess deposits for financial institution. The aforementioned capabilities would make possible the efficient and effective balance sheet rebalancing.

We are on a mission to develop a fully digital, syndication network that will allow smaller financial institutions to implement a continuous, fully digital and automated Balance Sheet Management solution with focus on managing capital adequacy, profitability, the associated and interrelated risks of assets and liabilities, various risks including interest rate, credit and liquidity under a broad set of potential economic shocks.

The solution should offer Fund Transfer Pricing for accurate loan pricing and cost accounting, and a Scenario Analysis capability which provides rebalancing recommendations to improve the capital adequacy, liquidity position, capital requirements, risk profile and profitability outlook of the Bank should the given macroeconomic scenario be realized.

The recommendations, while in full control of financial institutions, will drive sell/buy orders into the syndication network to provide automated Balance Sheet Management. It is also envisaged that the system over time will provide AI-generated narrative to provide executive summary of the balance sheet and recommendation for execution at various levels including CxO and BoD.


Unlock the power of Machine Learning to run and analyze various stress scenarios on your current portfolio and take advantage of Generative AI to interpret the observations into easy to read narrative.

Analyze your asset mix

Take advantage of over 25 years of lender data in conjunction with extended list of 35 key economic indicators and loan and historic loan performance data to observe likely future performance projections using Machine Learning applied to your balance sheet asset mix.

Rebalance your assets

Adjust the asset mix to address various concentration, interest rate and liquidity risks that are likely to impact your future performance and execute seamlessly.

Outperform your peers

Compare how your FI performs in relation to your real peer group of institutions with the similar asset mix. See how your asset-mix-peers performed in a period of historical stress.