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May 8, 2008

Probabilities are Better than Scores

Strategic Analytics slide from Fair Isaac Interact on 2007 mortgage meltdownDuring a panel at Fair Isaac’s Interact conference last week, a banker from Abbey National in the UK suggested that part of the credit crunch was due to the use of the FICO score.  Unlike other panelists, who were former Fair Isaac employees, this gentleman was formerly of Experian!  So there was perhaps some friendly rivalry, but his point was a good one.  He cited an earlier presentation by the founder of Strategic Analytics that touched on the divergence between FICO scores and the probability of default.  The panelist’s key point was that some part of the mortgage crisis could be blamed on credit scores, a point that was first raised in the media last fall.

The FICO score is not a probability. 

Fair Isaac people describe the FICO score as a ranking of creditworthiness.  And banks rely on the FICO score for pricing and qualification for mortgages.  The ratio of the loan to value is also critical, but for any two applicants seeking a loan with the same LTV, the one with the better FICO score is more likely to qualify and receive the better price.

Ideally, a bank’s pricing and qualification criteria would accurately reflect the likelihood of default.  The mortgage crisis demonstrates that their assessment, expressed with the FICO score, was wrong.  Their probabilities were off. (more…)


April 15, 2008

Adaptive Decision Management

In this article I hope you learn the future of predictive analytics in decision management and how tighter integration between rules and learning are being developed that will  adaptively improve diagnostic capabilities, especially in maximizing profitability and detecting adversarial conduct, such as fraud, money laundering and terrorism.

Business Intelligence


Visualizing business performance is obviously important, but improving business performance is even more important.  A good view of operations, such as this nice dashboard[1], helps management see the forest (and, with good drill-down, some interesting trees). 

With good visualization, management can gain insights into how to improve business processes, but if the view does include a focus on outcomes, improvement in operational decision making will be relatively slow in coming.

Whether or not you use business intelligence software to produce your reports or present dashboards, however, you can improve your operational decision management by applying statistics and other predictive analytic techniques to discover hidden correlations between what you know before a decision and what you learn afterwards to improve your decision making over time.  More...

This has become known as decision management, thanks to Fair Isaac Corporation, but not until after they acquired Hecht Nielsen Corporation.

Enterprise Decision Management


HNC pioneered the use of predictive analytics to optimize decision making.  Dr. Nielsen formed the company in 1986 to apply neural network technology to to predict fraud.  The resulting application (perhaps it is more of a tool) is called Falcon.  It works.

In 2002, Fair Isaac acquired HNC (for roughly $800,000,000 in stock) to pursue a “common strategic vision for the growth of the analytics and decision management technology market”.  But shortly before the merger, HNC had acquired Blaze Software from Brokat for a song following the Dot Bomb of October, 2000 – a month before 9/11.  This gave HNC not only great learning technology but, with a business rules management system (BRMS), the opportunity to play in broader business process management (BPM), including underwriting and rating (which is highly regulated), for example. 

Of course, the business rules market has since become fairly mainstream and closely related to governance, risk and compliance (GRC), all of which were beyond the point decision making capabilities of either HNC or Fair Isaac before both these transactions.

Once Fair Isaac had predictive and rule technology under one roof, bright employees such as James Taylor, coined “Enterprise Decision Management”, or EDM for short.

Predictive Analytic Sweet Spots

Before it merged with Fair Isaac, HNC’s machine learning technology was successful (meaning it was saving tons of money, not just an application or two) in each of the following business to consumer (B2C) application areas:

  • Credit card fraud
  • Workmen’s compensation fraud
  • Property and casualty fraud
  • Medical insurance fraud

Clearly fraud, across insurance and financial services is a sweet spot for decision management.  Today, that includes money laundering and, in general, any form of deceit, including adversarial forms, such as involving terrorism.

HNC also moved into retail and other B2C areas, including:

  • Targeting direct marketing campaigns
  • Customer relationship management (CRM)
  • Inventory management

Some of the specific areas in marketing and CRM included:

  • “Up-selling” (i.e., predicting who might buy something better – and more expensive)
  • “Cross-selling” (i.e., predicting which customers might buy something else)
  • Loyalty (e.g., customer retention and increasing share of wallet)
  • Profitable customer acquisition (e.g., reducing “churn”)

The inventory applications included:

  • Merchandizing and price optimization
  • SKU-level forecasting, allocation and replenishment

Predictive Analytic Challenges

The principle problem with predictive analysis is the care and feeding of the neural network or the business intelligence software.  This involves formulating models, running them against example input data given outcomes, and examining the results.  For the most part, this is the province of statisticians or artificial intelligence folk.

A secondary challenge involves the gap between the output of a predictive model and the actual decision.  A predictive model generally outputs a continuous score rather than a discrete decision.  To make a decision, a threshold is generally applied to this score. 

  1. Yes or no questions are answered by applying a threshold to a score produced by a formula or neural network to determine “true” or “false”.
  2. Multiple choice questions are answered using a predictive model per choice and choosing the one with the highest score.
  3. More complex decisions are answered as above using a predictive model that combines the scores produced by other predictive models.

In general, especially where decisions are governed by policy or regulation, predictive models and decision tables are combined with rules using one of the following approaches:

  1. More complex decisions are answered as above using predictive models that are selected by rules in compliance with governing policy or regulations.
  2. More complex decisions are answered using rules that consider the scores produced by predictive models in compliance with governing policy or regulations.

In general, governance, risk and compliance (GRC) requires rules in addition to any predictive models.  Rules are also commonly used within or to select predictive models.  And special cases and exceptions are common applications of rules in combination with predictive models.


A simple case of defining (or combining) predictive models is a scorecard.  The following example shows a scorecard from Fair Isaac’s nice brochure on predictive analytics that could be part of a credit worthiness score:

An exemplary credit scorecard from Fair Isaac

Fair Isaac is the leader in credit scoring, of course.  Their FICO score is the output of a proprietary predictive model.

The following example shows how Fair Isaac’s predictive model is combined with other factors in the mortgage industry (click it for a closer look):

Rate Sheet for Mortgage Pricing

Note all the exceptions and special cases spread throughout this scorecard!

This explains why business rules have been so popular in the mortgage industry.  Pre-qualifying and quoting across many lenders clearly requires a business rules approach (which explains why Gallagher Financial embedded my stuff in their software a decade ago).  Even a single lender has to deal with its own special cases and the bigger the lender the more there are (which is why Countrywide Financial>[2] developed its own rules technology, called Merlin, decades ago).

Decision tables

For anything but the simplest decisions, the results of predictive models are considered along with other data using rules to make decisions.  In some cases, these rules are simple enough to fit into a decision table (or a decision tree rendered as a table) such as the following:

Medical Test Decision Table for Life InsuranceBase Insurance Premium Decision Tree Table

Tables like the one on the left can be used during underwriting to determine what variables are appropriate for gauging the risk of death covered by a life insurance policy.  This demonstrates that rules (in this case, very simple rules) can be used to determine which predictive model (or inputs) to consider in a decision. 

Tables like the one on the right correspond to decision trees and can be used instead of scorecards to set the base premium for auto insurance.  Additional rules typically adjust for other factors like driver’s education classes, driving record, student drivers, and other special cases and exceptions.  This is similar to the use of notes in the mortgage pricing sheet shown above.

The point is that real decisions are not as simple as a single predictive model, a scorecard, or a decision table.  And once these decisions are defined and automated using any combination of these techniques, improving those decisions can seem overwhelming complex (just from a technical perspective!)

Predictive analytics is not enough for EDM

Enterprise Decision Management (EDM), discussed above, is all about this multi-dimensional decision technology environment (scorecards, decision tables, and rules) but also about bringing statistical and neural network technology in to improve the decision making process more easily and less manually or subjectively.  The Fair Isaac brochure referenced above, for example, has some nice graphics showing statistical techniques (such as clustering) and graphs showing interconnected “nerves”.

There are several aspects of decision making that not even magically successful machine learning will eliminate, however:

  1. The requirement to comply with governing policies or regulations.
  2. Special cases that cannot be learned for various reasons, including:
    • Limitations on the number of variables used in predictive analytics.
    • Poorly understood, non-linear relationships in the data
    • A lack of adequate sampling for special cases
    • A need for certainty rather than probability
  3. Exceptions that cannot be learned, as with special cases.

Of course, special cases and exceptions are common in both policy and regulation.  For examples, consider policies that arise from contracts or customer relationships or the evolutionary nature of legislation, as reflected in the article on the earned income tax credit.

Rules are not enough for EDM

On the other extreme, commercial rule technology has not been capable of adaptively improving decision management.  In fact, except when they are modified by people, the use of rules in decision management is completely static, as well as entirely black and white.  There is no learning with any of the business rules management systems from leaders like Fair Isaac, Ilog, Haley, Corticon, or Pegasystems.

Amazingly, there are no mainstream rule systems today that deal with probability or other kinds of uncertainty.  Without such support, every rule in the tools from the vendors previously mentioned is black and white.  This makes them very awkward for applications such as diagnosis.  And all decision management applications, including profit maximization and all forms of fraud detection, are intrinsically diagnostic.  Prediction results in probabilities!

The earliest diagnostic expert systems were developed at Stanford.  One used subjective probabilities to diagnose bacterial infections.  Another used more rigorous probabilities to find or deposits (it more than paid for itself when it found a $100,000,000 molybdenum deposit circa 1990!).  These applications were called MYCIN and PROSPECTOR, respectively.

This seems shocking really, since the technology of these systems is well-understood and technically almost trivial.  The truth is that the Carnegie Mellon approach to business rules has won because it dealt with “the closed-world assumption”, which means that it could handle missing data better.  But CMU’s approach was strictly black and white.  Stanford was left in the dust commercially after the success of OPS5 at Digital Equipment Corporation and the commercialization of expert systems at Carnegie Group, Inference Corporation, IntelliCorp and Teknowledge left uncertainty in the dust during the mid-eighties.  Neuron Data, which became Blaze, followed the same trail away from the uncertain toward the black and white of tightly governed and regulated decisions.

With nothing but black and white rules, EDM leaves it up to people to adapt the decisions.  Sure they can use predictive analytics, but there is no closed loop from predictive analytics involving rules.  Any new rules or any changes to rules follow the stand-alone business rules approach.

Innovate for Rewards with bounded Risk

One problem with black and white rules technology is that it forces you to be right.  This stifles innovation.  Ideally, you could formulate an idea and experiment with it at bounded risk.  For example, you could say “what if we offered free checking to anyone who opens a new credit card account with us” and test it out.  You don’t want to absorb the cost of thousands accepting your offer only to lose more on checking fees that you gain through credit card fees.  So you indicate how often or how many such offers can be made. 

Not surprisingly, this approach is tried-and-true.  It’s most common form is the champion/challenger approach.  Fair Isaac has been “championing” this approach for some time (see this from James Taylor).

But how do you close the loop?  How do the rules learn when this new option should be used to maximize profit?  The fact is, they don’t.  People do it using predictive analytic techniques and manually refining the rules.

The problem, once again, is that the rules do not learn and that their outcomes are black and white.  The rules do not offer a probability that this will be a profitable transaction.  And they do not learn whether a transaction will be profitable over time, either.  That’s up to the users of predictive technology and managers.

Adaptive Decision Management


 Adaptive Decision Management (ADM) is the next step in EDM.  In ADM the loop between predictive analytics and rules is closed.  At a minimum this involves learning the probabilities or reliability of rules and their conclusions.  This learning occurs using statistical or neural network techniques that can be trained, optimized, and tested off-line and – if your circumstances allow – even allowed to continue learning and adapting and optimizing while on-line.  For example, advertising, promotional (e.g., pricing) and social applications almost always adapt continuously.  Unfortunately, none of them use rules to do this yet, since the major players don’t support it!

Innovation and ADM

The adaptation of rule-based logic brings new flexibility and opportunity to the use of rules in decision management.  Adding a black and white business rule requires complete certainty that the rule will result in only appropriate decisions.  Of course, such certainty is a high hurdle.  Adaptive rules have a relatively low hurdle.

With adaptive rules an innovative idea can be introduced with a low, or even a zero probability.  As experience accumulates, the learning mechanism (again, statistical or neural) determines how reliable the rule is (i.e., how well it would have performed given outcomes).  The technology can even learn how to weight and combine the conditions of rules so as to maximize their predictive accuracy.  Without learning “inside a rule”, the probability of the rule as a whole may remain too low to be useful.  And, unlike a black box neural network, the functions that combine conditions and the probabilities of rules are readily accessible, whether for insight or oversight.

The overall impact of adaptive rules is that you can put an idea into action within a generalized, probabilistic champion/challenger framework.  And using techniques such as the subjective Bayesian method used in MYCIN or other more rigorous techniques as in PROSPECTOR, more patterns can be considered and leveraged with the continuously improving performance that EDM is all about.

The advantages of ADM include:

  1. the improving performance of EDM
  2. faster and more continuous improvement versus manual EDM
  3. a generalized approach to champion / challenger using probabilities
  4. better predictive performance than manually maintained scorecards or tables
  5. improved performance over black and white EDM by leveraging innovation adaptively

Although they haven’t told me about it explicitly, I would expect Fair Isaac to move in this direction first among the current leaders given their EDM focus.  I would not be surprised to see business intelligence (BI) vendors, perhaps SAS,  move in this direction, too.  I know it will happen since we are already working with one commercial source of adaptive rules technology.  Unfortunately, Automata is under NDA about their approach for now, but stay tuned…  In the meantime, if you’re interested in learning more, please drop us a note at info at haleyAI.com.  And if you see any issues or good applications, we would love to hear them.

[1]A nice dashboard from from Financial Services Technology (http://www.fsteurope.com/) using Corda (http://www.corda.com/)
[2]I recently helped Countrywide upgrade to our software, just as much for usability as performance improvements.

March 26, 2008

Agile decision services without XML details

Externalizing enterprise decision management using service-oriented architecture orchestrated by business process management makes increases agility and allows continuous performance improvement, but…

How do you implement the rules of EDM in an SOA decision service?  (more…)

March 11, 2008

Goals and backward chaining using the Rete Algorithm

I was prompted to post this by request from Mark Proctor and Peter Lin and in response to recent comments on CEP and backward chaining on Paul Vincent’s blog (with an interesting perspective here).

I hope those interested in artificial intelligence enjoy the following paper .  I wrote it while Chief Scientist of Inference Corporation.  It was published in the International Joint Conference on Artificial Intelligence over twenty years ago. 

The bottom line remains:

  1. intelligence requires logical inference and, more specifically, deduction
  2. deduction is not practical without a means of subgoaling and backward chaining
  3. subgoaling using additional rules to assert goals or other explicit approaches is impractical
  4. backward chaining using a data-driven rules engine requires automatic generation of declarative goals

We implemented this in Inference Corporation’s Automated Reasoning Tool (ART) in 1984.  And we implemented it again at Haley a long time ago in a rules langauge we called “Eclipse” years before Java.

Regretably, to the best of my knowledge, ART is no longer available from Inference spin-off Brightware or its further spin-off, Mindbox.  To the best of my knowledge, no other business rules engine or Rete Algorithm automatically subgoals,  including CLIPS, JESS, TIBCO Business Events (see above), Fair Isaac’s Blaze Advisor, and Ilog Rules/JRules.  After reading the paper, you may understand that the resulting lack of robust logical reasoning capabilities is one of the reasons that business rules has not matured to a robust knowledge management capability, as discussed elsewhere in this blog.  (more…)

March 5, 2008

Behind the CEP curtain – it’s about time, not the cache

TIBCO is the CEP vendor most focused on the market for business rules, as reflected in Paul Vincent’s post here.  Although I agree with Paul that rule vendors are not currently offering enough in terms of support for long-running processes, the conclusions that he draws in favor of considering a CEP alternative to a BRMS are not compelling yet.

Paul said that rules don’t address the following that are addressed by CEP:

  1. BAM (business activity monitoring) and the other BPM (business performance management)
  2. Complex-rule processing
  3. Customer-centric (portfolio-based) decisions / policies

I am sure Paul was just being flippant, but you may notice that there is a bit of a war going on between CEP, BPM and rules right now.  (more…)

February 25, 2008

Agile Business Rules Management Requires Methodology

Don’t miss the great post about his and Ilog’s take on rule and decision management methodologies by James Taylor today (available here). 

Here’s the bottom line:

  1. Focus on what the system does or decides.
    • Focus on the actions taken during a business process and the decisions that govern them and the deductions that they rely on.
    • In priority order, focus on actions, then decisions, then deductions.
  2. Don’t expect to automate every nuance of an evolving business process on day one – iterate.
    • Iteratively elaborate and refine the conditions and exceptions under which
      • actions show be taken,
      • decisions are appropriate, and
      • deductions hold true

Another way of summing this up is:

Try not to use the word “then” in your rules!

Do check out the Ilog methodology as well as the one I developed for Haley that is available here.  The key thing (more…)

January 8, 2008

Elicitation and Management of Rules, Requirements and Decisions

A manager of an enterprise architecture group recently asked me how to train business analysts to elicit or harvest rules effectively. We talked for a bit about the similarities in skills between rules and requirements and agreed that analysts will fail to understand rules as they fail to understand requirements.

For example, just substitute rules in the historical distribution of requirements failures:[1]

  • 34% Incorrect requirements
  • 24% Inadequate requirements
  • 22% Ambiguous requirements
  • 9% Inconsistent requirements
  • 4% Poor scoping of requirements
  • 4% Transcription errors in requirements
  • 3% New or changing requirements


December 11, 2007

Managing Semantics, Vocabulary and Business Rules as Knowledge

A client recently asked me for guidance in establishing a center of excellence concerning business rules within their organization. Their objectives included:

  1. Accumulate requisite skills for productive success.
  2. Establish methodologies for productive, reliable and repeatable success.
  3. Accumulate and reuse content (e.g., definitions, requirements, regulations, and policies) across implementations, departments or divisions.
  4. Establish multiple tutorial and reusable reference implementations, including application development, tooling, and integration aspects.
  5. Establish centralized or transferable infrastructure, including architectural aspects, tools and repositories that reflect and support established methodologies, reusable content, and reference implementations.
  6. Establish criteria, best practices and rationale for various administrative matters, especially change management concerning the life cycles of content (e.g., regulations or policies) and applications (e.g., releases and patches).

I was quickly surprised to find myself struggling to write down recommendations for the skill set required to seed the core staff.  My recommendations were less technical than the client may have expected.   After further consideration, it became clear than any discrepancy in expectations arose from differences in our unvoiced strategic assumptions.  Objectives, such as those listed above, are no substitute for a clearly articulated mission and strategy.  


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