Decision Support

Experience Management by Decision Support

Imagine a Decision Support tool where you have, at-the-ready, your entire organization’s Experience about all prior decisions your company has ever made – and their outcomes – good, bad or ugly.

With Saffron … you do.

Saffron helps you squeeze every ounce of prior Experience possible from your data – across your enterprise knowledge base, or residing in your business processes – and considers it on your behalf so you can make quicker, more informed decisions today.

Saffron Natural Intelligence Platform

The Saffron Natural Intelligence Platform operates in a dimension beyond the limiting rules and models of traditional business intelligence or data analytics tools. What’s different is Saffron works by memory based reasoning. That means it identifies all the attributes for a specific decision case, and quickly identifies other similar cases, so you can quickly know as much as possible – and use your prior experience – on the matter about which you’re making a decision.

Saffron’s history with Decision Support applications is extensive. In National Security work we’re involved with Intelligence, Surveillance and Reconnaissance operations. In the Manufacturing sector, we’re helping generate replacement optimization recommendations for non-repairable parts; and in Banking & Finance, we’re supporting deep analysis of suspended mortgage loans to identify best actions to shorten approval cycles, to name just a few.

How it Works

It’s very straightforward.

We start by understanding what you want to make decisions about, and the data sources you want included in the analysis. By identifying the known possible outcomes, such as “go/no go,” “approve/deny,” or for more complex decisions, multiple choices. By computing all the likely outcomes, showing similarities among prior cases, and identifying key factors, Saffron presents you with just the data you need to make more informed decisions. And as you make them, the system learns and remembers, enriching its capabilities for future use.

Here’s an example of how Saffron helped a customer company in the mortgage loans industry to apply Experience Management by Decision Support. The customer tried for two years, but failed to create a new suspended loans decision support tool using traditional rules-based and statistical methods. They then approached Saffron to discuss applying our associative memory technology to the problem. Their story follows.

Industry Use Case

Mortgage Loans Processing / Suspended Loans

Overview

In mortgage loans processing, suspended customer-loan applications can grow into lost revenue, costing banks a lot of money. Some customer-loan applications are suspended due to incomplete or inconsistent information; others because of problems with the property itself. But still others are suspended for reasons amounting to a “mismatch” with the approval rules, as pre-defined by the company’s loan processing software.

Mortgage bankers want suspended loans to re-enter the approval process as quickly as possible, both to insure good customer service and to achieve optimum revenue for the mortgage loan company. To do this, suspended loans must be carefully examined to determine how best to correct these inconsistencies.

“We tried for two years to solve this problem––and failed. Until Saffron.”

–– Financial Services Company

Decision Support Objective

Use the collective Experience of mortgage bankers, to reduce the volume of loans suspended and not closed in the mortgage loan approval process, empowering individual bankers to make faster revenue-impacting decisions.

Customer Situation

A successful, well-respected global mortgage loan company processes a high volume of mortgage loans daily. Their goal is to ensure every applicant is correctly evaluated in their pursuit of a mortgage loan. All loans are subject to strict rules in a well-defined loan approval processing system.

Many mortgage loan applications fall out of the approval process for failure to meet the “rules’ of the existing loan approval management system. When this happens a customer’s loan application is suspended. Thousands of loans can be in a suspended mode at any one time for a variety of reasons, e.g., applicants may forget to report all their income, or leave important information off their applications. Any combination of such things can cause a loan application to be suspended, and potentially rejected.

With thousands of unique loan applicants, it is challenging to quickly evaluate each loan, and know what requirements must be met to return the loan into the approval process. Each unapproved loan represents disappointed customers and significant dollars in lost revenue to the mortgage company. Plus, every additional day a loan remains suspended, the likelihood it will fail to close increases.

Saffron’s Solution

Saffron worked with the customer to create a new Experienced-based decision support tool using SaffronMemoryBase and Saffron REST APIs. The solution helps company mortgage bankers rank suspended loans according to how likely they are to close, recalling similar loans successfully closed in the past to suggest modifications for the bankers’ consideration, and highlighting the shared characteristics of these similar prior loans.

Saffron developed the Reasoning Methods REST API for decision support, which includes rank- orderering loans most likely to close; finding similar loans; recommending actions; explaining how/why those actions will work; and automatically learning from actions and outcomes. The customer developed the end-user interface for the mortgage bankers, leveraging Saffron’s REST API for ease of integration.

As the banker works with a suspended loan, SaffronMemoryBase incrementally learns the outcomes – the successes and failures – of each of the banker’s modifications and incorporates these outcomes into SaffronMemoryBase for immediate use by all other bankers working on new and existing suspended loans.

Using Saffron now enables this banking customer to apply the mortgage department’s sum-total Experience across all loan decision makers, giving each the Experience of many others. Saffron also defined an extensive new “set” of more than 125 unique loan-status attributes as decision criteria, all unconstrained by rules-based or statistical business intelligence models.

Results

The implementation is underway. Our goal is to improve suspended loan conversion rates by 3 – 5% annually. The net result is satisfied customers supported by better, faster loan decisions.

Technical Approach

Making decisions is about weighing the likelihood of outcomes to select the most favorable action. To enable a better decision, a Decision Support tool needs to compute likelihood across scores of possible outcomes, and make recommendations based on past cases.

The basic premise of Saffron REST APIs is to add easy-to-configure Decision Support capabilities to SaffronMemoryBase.

Saffron models the essence of your Decision Support process with three simple concepts:

1. Cases – A case is an application entity upon which decisions will be made. In the suspended loan example, a loan is a case. The characteristics of a case are represented as a set of properties called attributes.

2. Attributes – An attribute is a name-value pair representing a property. The possible results of acting upon a case based on a decision are represented by a set of labels called outcomes. In the suspended loan example, the properties of the loan are mapped as the attributes of the case.

3. Outcomes – In the suspended loan example, the final loan status of approved or denied is modeled as outcomes.

The key functions for Saffron’s Decision Support REST API include:

Triage – The rank ordering of events/transactions according to their likelihood of outcomes. Rank ordering will be based upon the Customer’s defined factors which may be numerous and large in scale.

Recommend – Saffron will recommend the actions most likely to result in favorable outcome based on the case’s similarity to other case with favorable outcomes.

Nearest Neighbor – Saffron will identify existing cases within SaffronMemoryBase that are most similar to the case under examination. Similarity analysis is based on the distance between the attributes of a case at a given point in time or status and their values at an outcome.

Explain – Saffron will support the rank ordering and recommendations with two forms of explanation. First, prior similar cases and actions will be presented as the basis of prior experience. Second, the most discriminant factors for the rank and recommendation will highlight the most important aspects of similarity and difference.

Adapt – Saffron will adapt and learn over time by considering additional case experiences that occur when cases are resolved. Additional case experiences will be captured on the fly for continuous learning and improvement of the rankings and recommendations, providing a dynamic experience-based decision support capability

Conclusion

In Saffron’s world, Experience Management by Decision Support focuses primarily on Reasoning by Classifications and Temporal Analysis REST APIs. These allow you to reuse the Decision Support capabilities built into SaffronMemoryBase across the enterprise. You can also support a variety of operations and easily integrate Saffron into existing and new end-user interfaces.

By mapping the entities for a given situation into these simple concepts, and ingesting the data into SaffronMemoryBase, powerful Decision Support functions result, and they help you and your company make better decisions.

For more information about Experience Management by Decision Support, contact us.