THE MST LOAN LOSS ANALYZER PLATFORM

Bringing Confidence to your Allowance Calculation and Documentation

The MST Loan Loss Analyzer (LLA) is your financial institution’s reliable, repeatable process for determining your allowance, under the current incurred loss standard and as you transition to CECL. The LLA satisfies regulatory requirements while eliminating the demands and concerns associated with manual processes.

A Powerful Solution

  • Loan Data Warehouse

    Compile and manage loan and loss data for current and historical periods.

  • Shadow Loss Analysis

    Test CECL methodologies while maintaining a compliant incurred loss model.

  • Correlation Analysis

    Calculate the relationship between economic variables and loan level data to support forecasts.

  • Model to Your Methodology

    Choose your model or models. Manage loss rate methods, roll-rate methods, probability-of-default methods, or methods using vintage or other cohorts.

  • Verifiable Audit Trail

    Verify the accuracy of the calculation so auditors and regulators can see exactly what you did and why you did it.

  • Accuracy

    Eliminate errors inherent in manual data entry and Excel spreadsheets so you can be confident that your calculations are accurate.

  • Systems Integration

    Interface with your existing data systems, seamlessly gather data, and calculate the allowance according to your chosen methodology or methodologies.

  • Robust Reporting

    Produce reports that serve as management tools to make sound business decisions and maximize profitability.

How It Works

Loan Loss
Analyzer
Platform
  • Data Warehouse

    Consolidate data sources and life of loan management.

  • ASC 450-20 & ASC 310-10 Management

    Manage pooled and specific reserves (incurred loss methodology).

  • Migration Analysis

    Migrate losses based on loan movement to determine more quantifiable loss rates.

  • Probability of Default / Loss Given Default (PD/LGD)

    Migrate loss based on loan movement to return an individual PD and LGD for determining expected losses.

  • Vintage and Cohort Analysis

    Allows individual loans to be tracked on a wide variety of different data points to establish long-term cumulative loss rates for loans in a specific vintage or cohort.

  • Qualitative Factors

    Support objective and quantified adjustment for current conditions.

  • Virtual Economist
    (Correlation Analysis)

    Correlate analysis of economic data to loan level data in support of forecasts.

  • Shadow Loss Analysis

    Test alternate methodologies in a dual environment (especially helpful in transition to CECL).

  • Acquired Loans

    Manage ASC 310-20 and ASC 310-30 pools to calculate additional reserves, evaluate remaining discount adequacy, and create call reports and footnote disclosures.

  • Stress Testing

    Test capital adequacy according to bank-selected stress scenarios and apply the resulting impact to capital levels.

  • Robust Reporting

    Client-Defined Reports, Report Writer, Standard Reports with details, ALLL, CECL, Concentrations, ASU 2010-20, Call Reports, Data Applicable reports Problem Loan Reporting and Specific Impairments Reporting.

  • Review & Approved Workflow

    Integrate and streamline credit review functions though a sophisticated system of inter-related modules.

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"It is easier to illustrate through the use of the Loan Loss Analyzer reports the up-front provision costs of making loans, which can assist in the pricing decisions related to those loans."
Joseph Polaczyk
“The use of the MST software has greatly enhanced our institution’s ability to report and analyze information in a much more detailed fashion. The system has ‘passed’ both internal and external auditors. As a SOX system, there is significant re-programming and testing time each time the reports need to change, however the flexibility in the MST programming continues to meet our needs.”
Don Mayo
“Shadow Loss Analysis allows us to run migration and PD/LGD models in a parallel environment. I can look at how the allowance calculations differ and how they react with different variables. This helps the CFO to inform the board and manage the capital of the bank for better decision making.”
Kristen Deitrick 

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