It takes all kinds of AI and Humans to make Good Business Decision

Presented By
Denis Gagne, CEO & CTO, Trisotech
Simon Ringuette, R&D Lead, Trisotech

In today’s rapidly evolving markets, the integration of human insight with advanced AI technologies is crucial for making sophisticated, timely decisions. This presentation delves into how businesses in regulated industries such as finance, healthcare, and government can leverage AI to balance mission-critical risks with profitability, ensure compliance, and maintain necessary transparency. We’ll explore strategic, tactical, and operational decisions across various scenarios, demonstrating the power of AI to augment human decision-making processes, thus optimizing outcomes. Whether you are looking to enhance your existing protocols or build new frameworks, this webinar will equip you with the insights and tools to advance your decision-making capabilities.

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Business Rules and Decision Management in Healthcare

The Mortgage Feature Set (MFS)

For organizations that underwrite and sell mortgages, Trisotech is enhancing its Digital Enterprise Suite (DES) with mortgage industry specific (Regulatory and GSE) extensions.

The Mortgage Feature Set (MFS) option offers fully integrated features and functions that allow organizations to model and automate their decisions and workflows more efficiently.

Critical GSE and Regulation Z decisions and workflows are made available. These critical rules and supported decisions are gleaned from more than 500 pages of GSE and Federal Regulations.

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Watch our Recorded Demo Watch our Recorded Webinar - Creating Low-Code Loan Applications using the Trisotech Mortgage Feature Set

MFS Sample Models

For mortgage underwriters/creditors that sell their mortgages to Fannie Mae or Freddie Mac, all the critical decisions mandated in Fannie Mae’s Selling Guide and Freddie Mac’s Seller/Server Guide are included for the conventional and HomeReady™ products.

Fannie Mae Sellers Guide Knowledge Models

  • Fannie Mae Conventional Products
  • Fannie Mae HomeReady™ Products

Fannie Mae Sellers Guide Required Decision Models

  • Complete Sellers Guide Decision model for pre-qualification and preclosing
  • B2-1 Loan-to-Value (LTV) Ratios
  • Loan Level Price Adjustment
  • B5-1 High-Balance Mortgage Eligibility and Underwriting
  • B3-5.1 General Requirements for Credit Scores
  • Eligibility Matrix
  • B7-1 Mortgage Insurance Coverage Requirements
  • B3-1 Manual Underwriting

Freddie Mac’s Seller/Servicing Guide Models

  • Complete Sellers Guide Process model for pre-qualification, preclosing, and presales
  • 4603.2 Super Conforming Mortgage Eligibility and Underwriting
  • 4203.2 Loan-to-value (LTV) Ratios (Calculation of LTV Ratio)
  • 5203.1&2 Indicator Score and General Requirements for Credit Scores
  • 4702.1 Mortgage Insurance Coverage Requirements
  • Exhibit 25 Risk Class and/or Minimum Indicator Score Requirements
  • Exhibit 19 Credit Fees

Also included is the important Ability to Repay the decision model from Regulation Z.

Regulation Z

  • Regulation Z decision models
  • 12 CFR 126.43 Ability to repay (ATR) Decision Model
  • 12 CFR 126.43(C)(2)(vi) Monthly debt to income ratio

MFS Knowledge Features

The Mortgage Feature Set provides various common concept models that can be re-used for mortgage model creation. These concepts can be dragged-and-dropped into decision models (DMN), workflow models (BPMN), and case model (CMMN). The concepts can also be linked to any terms in the labels of various model elements. Users can modify the pre-configured entries and create additional entries.

A number of such common concept models include:

Uniform Mortgage Data Program® (UMDP®) Knowledge Entity Model

This Knowledge Entity Model contains concept maps along with definitions of the various concepts associated with Uniform Mortgage Data Program® (UMDP®). The Uniform Mortgage Data Program (UMDP) is an effort undertaken jointly by Fannie Mae and Freddie Mac at the direction of the Federal Housing Finance Agency (FHFA) to enhance mortgage data quality and standardization.

Mortgage Acronyms Knowledge Entity Model

This dictionary of mortgage terms and acronyms was created from the Uniform Services Veterans Mortgage list of mortgage terms and acronyms.

Other concept models are also available in support of provided specific mortgage workflow and decision models.

MFS MISMO Specific Features

The Mortgage Industry Standards Maintenance Organization (MISMO) is a not-for-profit, wholly owned subsidiary of the Mortgage Bankers Association (MBA) responsible for developing standards for exchanging information and conducting business in the U.S. mortgage finance industry.

MISMO Glossary

This Knowledge Entity Model contains a set MISMO related concepts sourced from the MISMO Business Glossary-Term List. Each provided term comes with a definition and source. Focus areas include:

  • Appraisal
  • Closing Disclosure
  • Commercial
  • Data Governance and Management (DGM) Fit For Purpose
  • eMortgage Glossary
  • MISMO Logical Data Dictionary (LDD)
  • Loan Application
  • Loan Delivery
  • MBA Glossary
  • MISMO3.4 iLAD
  • MISMO Approved Acronym
  • MISMO Life of Loan
  • Origination
  • Servicing

Predefined MISMO Data Types

Trisotech provides out-of-the box MISMO aligned data types that can be assigned with one click to elements in Decision models (DMN), Workflow models (BPMN), and Case models (CMMN).

The MISMO Accelerator

The MISMO Accelerator is a comprehensive set of predefined MISMO element structures provided as reusable drag and drop data structures. Simply drag the desired MISMO element to a Decision (DMN), Workflow (BPMN) or Case (CMMN) modeling canvas to obtain an element of that type.

MFS Additional FEEL Functions Features

A complementary library of FEEL functions is included in the Mortgage Feature Set to make the development of financial analysis logic more efficient. Examples of functions added with the Mortgage Feature Set option include:

  • annual interest rate
  • annual nominal interest rate
  • asset depreciation
  • cumulative interest payments
  • cumulative principal payment
  • decimal fraction to decimal number
  • decimal number to decimal fraction
  • depreciation for accounting period
  • discount as percentage
  • effective annual rate
  • first coupon date after settlement
  • fixed rate declining balance asset depreciation
  • future value
  • future value schedule
  • installments interest
  • interest payment
  • internal rate of return
  • last coupon date before settlement
  • modified internal rate
  • net present value
  • number of coupon dates between settlement and maturity date
  • number of coupon days in coupon containing settlement date
  • number of days from settlement date to first next coupon date
  • number of days in coupon from beginning to settlement date
  • number of periods
  • particular payment
  • payment
  • present value

Mortgage Feature Set (MFS)

Mortgage Feature Set (MFS) is an option of the Digital Enterprise Suite

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From Laws and Regulations to Decision Automation

Providing complete traceability and accountability.

Presented By
Tom DeBevoise
Simon Ringuette

Regulations are a set of obligations that apply to corporations and individuals. They can be established through laws or under the authority of a governing body. Regulations may explicitly define processes and rules, but often they prescribe outcomes or performances without detailing how to achieve them.

When an organization must comply with a regulation, it aligns its operations with the obligations specified in the regulation. Compliance is the action of ensuring this alignment. However, demonstrating compliance can be a challenge because organizations must be able to trace their implementation back to the regulation.

To create traceability, a knowledge entity model (KEM) is developed. This model represents the regulation using vocabulary, concept maps, and business rules. The KEM is derived from the text of the regulation, breaking it down into vocabulary terms, concept connections, and business rules.

Using the KEM, an automated solution can be created using decision automation and business process automation (DMN and BPMN). This solution links the business rules to the decision or process as a knowledge source, creating a traceable solution.

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