ZM Financial Systems: Simplifying Application of Assumptions

CIO VendorButch Miner, Co-founder
A $30 billion bank was overspending on their risk and regulatory management efforts. Several models were being used for similar analytics but delivering inconsistent results. This prompted the bank to explore ways to stop the redundancy of data and improve operations. Three data-related issues surfaced:

1) Redundancy of data and assumptions
2) Inconsistencies in data and the subsequent need for reconciliation
3) A lack of seamless third-party integration

ZM Financial Systems (ZMFS) assisted the bank in reorganizing its entire system, delivering a unified platform with consistent data and strong governance. The bank is now able to model and mix behavioral default models at the record level. Each record is tagged with identifiers for asset and liability management (ALM), Dodd-Frank Act Stress Test (DFAST), liquidity coverage ratio (LCR), and budgeting. One set of data and assumptions are used to generate consistent cash flows that can be reaggregated for different types of analysis and risk reporting. As an example, ZMFS tailored the flow of third-party credit assumptions into regulatory capital calculations and credit-adjusted portfolio targets. This drove allowance for loan loss calculations before being paired with the legacy ALM, automating the balance sheet and income statement generation as well as integration into the DFAST 10-50 report.

ZMFS believes financial institutions must have access to stronger analytics to perform confidently in an uncertain economic world. Butch Miner, co-founder of ZMFS, notes that in the quest to deliver creative product offerings and attract more business, banking institutions have become susceptible to strategic risks. “Our analytical tools leverage instrument details, assumptions, and mechanics to avoid duplication of efforts and ensure consistent application of assumptions,” says Miner.

Our analytics tools leverage instrument details, assumptions, and mechanics to avoid duplication of efforts and ensure consistent application of assumptions

“Clients come to us because we can provide peace of mind when they are running their analytics to manage these risks and meet related regulatory requirements.” ZMFS’ range of analytical tools fulfills these requirements by delivering ALM, portfolio, and budgeting analysis through either a hosted or in-house environment. The analytical suite allows financial institutions to construct and test various scenarios so they can manage their risk/reward tradeoff. Scenarios and assumptions can be internally-generated or seamlessly integrated from third-party solution providers.

On the regulatory side, ZMFS’ economic scenario capabilities assist financial institutions in demonstrating compliance. The solution includes DFAST scenarios and expanded default modeling which helps clients in creating an efficient and effective DFAST submission process. The evolution of historical allowance modeling into the current expected credit loss model (CECL) has increased a bank’s regulatory burden, augmenting its demands for robust asset and liability management software.

“The unique reporting capabilities help financial professionals to prepare for audits by submitting reports for regulatory requirements, and providing secure information to their stakeholders,” adds Miner. “Our tools allow clients to strengthen their analytics and quickly respond to various regulatory requirements while efficiently delivering actionable information.”

To remain one step ahead in the market, the firm involves itself in regular peer exchanges and conversations with regulatory agencies to react and respond to their clients’ market needs. The end goal: help financial institutions avoid economic risk and maximize their profits. ZMFS continuously looks ahead to layer more intelligence in their product which can take advantage of available data. “Intelligent automation is gaining ground as models “learn” from history. We aim to apply that knowledge to forecasting as well,” informs Miner.