RMEx was conceived in the beginning of 1990. The collection industry was in desperate need of new ways to be more productive in the core operations within the collection cycle. We recognized the trend towards lower fees, higher costs and more demanding clients. This meant that in the absence of a miracle, we would have to look to the solution that was rescuing the major auto-makers and IBM from their financial woes – technology. We had identified several areas that needed attention in order to offer us the chance for a quantum leap in productivity. These areas were –
By finding solutions to these problems, we would have succeeded in significantly increasing our productivity. How else could we work an account at a fee of 20% and make a profit? How could we address most of these challenges with the existing technologies? In 1990, Quantrax came to the conclusion that we could not! Interestingly, most of the collection experts we talked to were in complete agreement with our conclusions, but were willing to accept that there was no better way to address the challenges!
Enter artificial intelligence and expert systems. “Expert systems” are computer programs that can mimic the behavior of a human expert. They are ideal in environments where the experts are scarce or expensive and when decisions have to be made based on a very large number of rules. Artificial intelligence is a term that was created to differentiate it from “human intelligence”, which in spite of its great strengths is also associated with lapses in memory, inconsistencies, fatigue and the need for vacations! In 1990, there were no commercially available systems that could have been described as expert systems, (business applications that are based on artificial intelligence) and Quantrax Corporation created a proposal for, designed and built a new system that would have artificial intelligence as its foundation.
To understand the product and the development process, one must appreciate the difference between traditional data-based systems and expert systems (Also called knowledge-based systems). With data-based systems, we have computer programs that act on data and produce results. The data and programs are closely related, and to change the behavior of the system you must change the programs. As an example, you may have determined that a certain letter, if sent within 50 days of placement, in certain geographical areas, produces successful results. You would change your programs to generate the required letter at the correct time for the selected accounts. But what if you later wanted to expand the range of zip codes, or change the day on which the letter was sent, or target certain balances? You need more program changes!
In a traditional data-based system, data and programs are closely related. Changing the way the system works, usually requires program changes.
With an expert system, there are users who provide input. In addition, there are rules. A complex computer program called an “inference engine” takes a user’s input, looks up the rules (stored in a knowledge-base), analyzes the circumstances and then makes intelligent decisions. Compared to data-based systems, expert systems allow you to quickly change the way in which you do business by changing the stored “rules”. This does not require any programming changes! To make decisions that would compare in scope and quality to those of a human would take thousands of rules and a complex inference engine, and this is one reason that expert systems have not found their way into many commercial business applications. Expert systems, while offering great potential, are very expensive to produce and need powerful computers to run on.
With a knowledge-based system, the behavior of the system can be altered with changes to the knowledge base. Program changes are usually not required.
RMEx allows a user to decide how they want to manage their accounts and then set up simple or complex rules to make sure that the necessary actions take place at the appropriate time. Decisions are made as the account progresses through the collection cycle, but the user controls the level of decision-making that is entrusted to the system. If more decisions are made by the system, the levels of automation are higher, and greater overall productivity can be expected. In our experience, while most users realized the value of allowing the system to make more decisions, this did not happen for some time. Why? Because most companies did not know exactly how each account should be worked! E.g. In the case of a medical account that had insurance, and now has a self-pay balance of $300, when should we stop working the account? After 3 contacts? Maybe 3 attempts and 2 contacts? What if there is a social security number and a place of employment? Should we try to obtain a credit report? And if the debtor has other accounts, should we consider what happened with those accounts? Most collection managers will agree that if you have 40 collectors, you would probably get at least 25 different answers to this problem. What is the correct answer?
Artificial intelligence allows you to build consistency into your work plans. The same condition can be handled in the same manner, regardless of the experience of the collector working the account. It can make everyone an expert! Concepts such as “fuzzy logic” can also be incorporated into collection software. Fuzzy logic attempts to replace “true or false” logic with computing based on “degrees of truth”. This can also be described, as analyzing “shades of grey” when something is not “black or white”. As an example, most collection systems will attempt to “link” new accounts to existing ones based on different criteria. Examples of the criteria used are debtor names and addresses. Addresses that “do not match”, such as “7200 Annandale Rd.” and “7200 Annandale Road” can be identified as being the same, by using fuzzy logic. This fuzzy thinking can be extended to determine when “Paul R. Smith” is the same as P.R. Smith” or the phone numbers (703) 255-6856 and 255-6856 belong to the same person.
There are some interesting secondary benefits that were derived from utilizing intelligent software.
A question asked by many people is “Why can’t I change my existing system to make decisions and provide the same functionality as RMEx”? Data-based systems can not be “changed” to be expert systems. Changing your programs to make some decisions will make your system more flexible, but that is not the same as having an “intelligent” system. An intelligent system must have the ability to consider not a handful or even hundreds of conditions, but often, thousands of possibilities. It must be able to analyze circumstances that affect the outcome of certain actions. Take the example of the decision that we would make to give up on a $150 balance after 2 contacts and no payment. While this would be a starting point, it cannot handle every situation. Consider the following exceptions that could be handled by an intelligent system.
With any advantages, there will also be disadvantages. In the case of deploying intelligent software, the following have been the most significant obstacles we faced.
Did RMEx’s clients derive the benefits that were projected? In most cases they did. In almost every case, it permitted the companies concerned to better understand and redefine their businesses. As the levels of automation were increased, the over-working of accounts was reduced, and more work was done with the same number of people. Fewer accounts remained “un-worked” and the quality of work done on each account was higher. If revenue per collector was a method of evaluating a collection operation, most RMEx users have enjoyed increases that exceed 30% – gains on this scale can not usually be approached by traditional data-based systems. |