Differentiating in the Commoditized Skip Tracing Market

Some time ago, the process of locating and gathering information about a debtor, or skip tracing, became a commodity. It is easy to get the information and the demand remains high. However, there is little differentiation among those companies providing the data. When an end user is presented with new address or phone number data in the collection software, rarely do they know where the information came from. It is true some collection software platforms can track or flag the source of the data, but the source cannot be derived by simply looking at the data. Let’s look at the differentiation available and some ideas for agencies to streamline their skip tracing processes.

Skip tracing service providers offer a variety of solutions. Free services, one-time fee services, real-time services and subscription services are commonly offered. Free skip tracing should be discarded. Although the obvious economic reasons are appealing, free skip tracing requires a staff, a high volume of manual intervention, inconsistency in effort and procedure and integration with the collection software, which is non-existent. You get what you pay for and free usually doesn’t cut it. We have all heard this before.

One-time fee services are commonly referred to as batch skip tracing. Batch mode is generally very consistent and integrated. The data is requested and received one-time, usually overnight, and automatically fed into the collection software. An agency can and should build all sorts of data handling rules to not only electronically load the data into the system but also set a level of reliability on the data and whether or not a verification process is required. What’s important is capturing all the data. After all, you are paying for it.

Batch is a good method when the agency has a need to process many debtors and having the updated information immediately is not important. Real-time services are similar to batch in terms of consistency and integration. The difference is in the timing. Real-time skip tracing is the fastest way to get updated or receive new information. It is usually accomplished in the collection software using a preprogrammed button or workflow where a request is sent to the service provider and a response is received real-time or near real-time. The concept of the data going straight into the software and the data handling rules as used in batch mode can apply.

A subscription model can be implemented for both batch and real-time. The beauty of a subscription model is that any debtors sent to the skip tracing service provider are “monitored” regardless of whether the request is sent in batch mode or real-time mode. This is attractive to agencies because the technology is always on. The provider will deliver a response on the initial request and monitor the debtor record for a predefined term. If at any time during the term the provider receives new information, it will automatically be delivered to the respective agency. Due to the nature of the service, the subscription model is commonly referred to as “monitoring.”

Excluding free services, technology and automation is largely well-defined and reliable in the skip tracing market. However, skip tracing can be counterproductive. Too much manual intervention and the mixing of service providers can be expensive in terms of dollars and man hours. I believe 99% of all skip tracing efforts need to be electronic and automated in the collection software. Agencies should work to streamline skip tracing efforts by simplifying or eliminating waterfall models in an effort to find one sound service provider that can satisfy all the service models previously mentioned. There are providers who offer batch, real-time, subscription, and manual skip tracing services. While the first three are electronic and integrated with collection software, the manual service is simply a portal where the service provider allows end users to manually search the database for new information or to maintain the customers monitored database. Working with a provider like this can greatly streamline an agency’s skip tracing process.

Regardless of your skip tracing practice, don’t overlook the information from the original creditor. It is usually the most reliable. You want that information, so ask for it in the placement file and load any data point you receive somewhere in your technology enterprise. If you don’t need it today, it will be easier to locate when you do need it tomorrow.

* This article is also published by Collection Advisor

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Automatic Collections

Nearly ten years ago, I read a book titled “The Automatic Millionaire” by David Bach. It is a lesson on accumulating wealth and achieving financial independence. The subject is not important here. What is important is how it is accomplished because we attempt to apply the same concepts in accounts receivable management and I believe it is particularly imperative when working telecom paper. That theory is “make it automatic.” In the book, the easiest way to achieve financial independence is to make the process automatic through the use of automated payroll deductions, account rebalancing and other such processes. Then, we do not have to do anything or really even think about doing anything. It just happens.

When it comes to timing, of course you do not have 20 to 30 years to collect on any one telecom account but what if we could apply the automatic practice to the collection efforts? What would that look like and how could your technology be the workhorse?

Voice and data providers are always competing to reduce costs and come up with efficient ways to manage customer payments. The service has become commoditized and thus makes it simple for customers to switch providers with little care for any current or past due balances. According to multiple sources, including a white paper published by HCL Technologies, the telecom industry loses in excess of $10 billion annually due to bad debt. That number is much too large to ignore the “make it automatic” concept.

Before automating anything in your software, design a flow that makes sense. It may be helpful to refer to this as a schedule of action. Allow me to explain the automatic setup in your software using the following flow as an example:

Investigation

Setup your system to begin investigative efforts immediately. The new business process should be constructed with logic to identify consumers with missing information. You may discover some accounts are missing too much information for collection attempts. For the rest, make sure the workflow in your software identifies consumers for immediate skip tracing and scoring. Labeling these consumers with a tag or status will make it easy for an automated interface to pick them up for transfer to your skip tracing or scoring service provider.

Dialing and Sending Notices

Like the investigative efforts, the workflow in your software should be able to automatically label consumers for required notices and be scheduled to either print the notices or transfer the requests to your letter vendor. Additionally, whether using a dialer or manually dialing, ensure the dialing pools built by the software are based on your secret sauce (balance, geographic location, credit score and so on).

Reminders

As a consumer, telecom bills are one of the easiest payments to overlook. This remains true for telecom accounts in collections as well. Setting up time-based events as reminders for consumers is imperative. Make sure your software is configured to automatically issue urgent letters, send emails or dial the consumer over the course of attempting to collect on the account.

Disconnect

When calls, notices and reminders are ignored, it is helpful to have a time-based event in your software to commence the disconnect process. This could be a simple pop-up when the account is accessed or an automated message to the service provider to cut the service. If the consumer is already in collections and still has full or partial service, nothing should get their attention like disconnecting the service entirely.

Blacklisting

When all other automated attempts fail, blacklisting is the most aggressive action. Here again, a time-based event should be configured in your software to commence this process. This will eventually require some manual intervention but knowing when to start the process and perhaps some initial actions can be automated. Blacklisting can follow a few different paths. It may involve hot-lining by redirecting all calls from consumers to a special department or in more extreme examples, include barring the consumer for service not only from one provider, but from all providers of similar telecom services.

Applying the “make it automatic” concept to telecom collection efforts may not make you a millionaire but it could certainly ease the effort of working your share of the $10 billion of bad debt every year in the telecom industry.

* This article is also published by Collection Advisor

Managing Unstructured Data with Big Data Notions

Data is traditionally structured. It makes the data easy to read and simple to consume. Are those statements actually true? I have a hard time accepting that perception. What if I told you we collectively perceive data as traditionally structured because we select to ignore unstructured data? All that remains is the structured data and because that turns into the primary focus, it becomes natural to claim data is traditionally structured. It makes the data easier to read and simpler to consume.

Unstructured data is defined as information that does not have a pre-defined model or is not organized in any sensible manner. This is not a new phenomenon. However, the sheer quantity of unstructured data is on the rise across all industry verticals and there is growing interest in the ingestion of that data. One of the largest examples of this is in the healthcare space. Take a minute to think about all the pre-printed medical forms, questionnaires, insurance forms, procedure and diagnosis forms, and claim forms you are exposed to as a patient or guarantor. That is what you see. Those same forms can exist in a variety of formats and layouts based on the insurance company, hospital or medical facility, or the state or locality where the services are performed. It may be a single field variance or a completely different looking form altogether. Whether typed into an electronic form or scanned into a system as an image, the data is likely unstructured making it nearly impossible to develop software to read, recognize and accurately ingest all of the data all of the time.

Instead of setting unstructured data aside, we need to use technology to reduce the constraints and structure the data. This will allow for the consumption of more data, which is really what enterprises are after. By understanding the data, you are creating a modern architecture ideally easy to use, repeatable, and secure. What could you accomplish with more data? What could society accomplish with more data, specifically in the healthcare space? Could we prevent outbreaks? Could we cure disease?

All enterprises deal with unstructured data. Some have more than others but unstructured data does exist everywhere. There are many methods of thinking about unstructured data, getting it into a structured format, and ultimately loading it into the system or data warehouse. It can be done manually with users reading and inputting the data, it can be done electronically with extract, transform, load-type processes to deal with most of the data, or it can be a combination of electronic and manual effort. Making the process as electronic and systematic as possible will be very helpful but remember it is nearly impossible to consume 100% of the data this way. Some manual effort will still be required when a form field is blank or has been moved to the next box, for example.

You need the right challenges while making the decision to ingest and make sense of unstructured data. It is important to know or at least question what you are possibly missing or what you can possibly gain from this additional data. Do not go blindly into the effort of managing unstructured data. Also, do not get caught up in the immediate issues or only a few use cases. Architect the process for a long term and comprehensive build out. You may know the challenges you are facing today but consider what challenges you may face down the road. Regulation, security, and technology are examples of ever-changing items in our industry that may present future challenges. Finally, data governance must be addressed. This is particularly true in the healthcare industry. The days of only a few fields being deemed personally identifiable information (PII) are fading away. If you are dealing with healthcare data, you are better off considering everything as PII. Secure access to the data on a need-to-know basis or at least with the vision that not everyone requires access to everything.

The concept of ingesting unstructured data is relatively new in the debt collection industry. Much of what I have presented crosses the line between what is considered data integration and what is considered Big Data. Managing unstructured data requires integration processes but understanding why unstructured data is important is a Big Data thought. Regardless of your approach, plan to pay for the sins of the past. Not capturing all the data and randomly dropping data into fields that “look” good may end up skewing your results as you ingest unstructured data.

* This article is also published by Collection Advisor

Managing Unconventional Skip Tracing Data

The common skip trace practice many third-party agencies follow after loading a new placement file is to address skip trace, phone number skip trace, bankruptcy skip trace, deceased skip trace, and credit score. Most collection professionals are well versed on this now monotonous routine. All agencies do it. The difference lies in how the return data is evaluated, captured, and embedded into the daily workflow for managing the inventory. However, there are some other, unconventional skip tracing services and often, they will provide some very revealing information. This information may be just enough to separate you from the competition. I would like to detail three unconventional skip tracing services and more importantly, ideas for how to manage them in your receivables management software.

Possible Incarceration

You may eventually discover a consumer is incarcerated over the course of working the account but have you considered running a skip trace for incarceration before any effort goes into contacting the consumer or sending a letter? Executing this skip trace and identifying a consumer is incarcerated will allow your representatives to focus on accounts that are more collectible. The possible incarceration skip trace should be scheduled one of two ways. First, the request can be automated to run with your normal skip trace service requests (address, phone, bankruptcy, deceased, credit score). Alternatively, you have your regularly scheduled requests run first and then based on those results, segment the ideal consumers for a possible incarceration skip trace. For example, if you receive a return that a consumer is deceased or has a credit score less than 400, your automated workflow may be set up to automatically close the account. In those cases, your skip tracing automation should bypass that consumer altogether for the possible incarceration scrub.

When receiving a hit for incarceration, some decisions need to be made. Most often, a release date (if applicable) is provided with the incarceration hit. Depending on the duration of the incarceration, you may option to close the account and re-open it immediately after the release date. On the other hand, you may just want to close the account and archive it. Either way, let your system handle it with built-in automation.

Possible Litigious Debtor

The litigious debtor skip trace could save thousands of dollars in legal costs. This may be one of the most fascinating services available these days. A returned hit on this service may indicate you are facing a consumer who has filed one or several lawsuits against agencies. It exists because consumer lawsuits against agencies and first parties are on the rise. Furthermore, consumer initiated lawsuits are steadily increasing because more and more consumers are becoming well educated on the FDCPA and other federal or state regulation.

Unlike the possible incarceration skip trace, you may want to set up workflow to run the possible litigious debtor skip trace right away. If a hit is returned, most agencies establish rules in the software to immediately flag and close the account in an effort to eliminate any contact whatsoever. When going this route, utilize workflow or other automation to close the account, remove it from any dialing queues, stop any other skip tracing efforts, halt all contact attempts, and flag the consumer so it is understood to steer clear of this account.

Property

This skip tracing service is a fun one and can arm your representatives with some very valuable information prior to contacting the consumer. The property skip trace will inform you if the consumer has recently purchased, leased, or somehow acquired new property. A house, condo, and land are all property examples. This information is especially valuable when a representative is working a consumer who indicates they are on a tight budget or simply does not have the financial resources to repay the debt.

As for automating this service, it is best to send the request after your first blast of skip tracing. You will want to carefully segment and select the consumers you want for the property skip trace. Construct the workflow to select consumers with a valid phone number and address, populated with full SSN, an outstanding balance that justifies the property skip trace, and a moderate to high credit score. Unlike possible incarceration and litigious debtor, turning on monitoring is a great idea for this service. This will allow your skip trace provider to continuously seek property hits for the consumer, rather than a one-time scrub, and notify you when a new hit becomes available. With this information, a representative can easily counter an excuse-ridden consumer by simply questioning his financial position since he has recently purchased a new property.

Before jumping right into unconventional skip tracing, you must first develop guidelines for isolating and marking the consumers for these different services, for identifying them when hits are received, and for the special handling they require. Regardless of the decisions for implementing unconventional skip tracing or managing the returns, store the data in your software or data warehouse and make the process automated in your software by including it in your everyday workflow.

* This article is also published by Collection Advisor

Synchronizing Collection Software with Hospital Data

As a server of medical accounts, agencies often find themselves facing some additional technology concerns. Every vertical brings its own technological challenges. In most verticals these challenges are shared but the healthcare vertical adds complexity when considering items such as system of record and the detailed nature of industry accepted data standards.

Medical collection software exists for any serious agency in the healthcare space. However, I see and hear agencies are simultaneously working in two different systems, their own collection software and the hospital’s system. (Throughout this column, I will repeatedly reference the “hospital’s system” but the terms “medical facility’s system,” “insurance company’s system,” “claims management system,” and so on could easily take its place.) The questions I will address in this column are: Why are there disparate systems and how can these agencies work out of one system? Most often the “why” is one of three scenarios. One, the hospital requires the agency to work directly in their system. Two, the agency software does not have the same data points as the hospital’s system. Three, there is no seamless integration between the two systems so real-time updates are not sent back and forth between the two pieces of software. In any case, system of record becomes a question. When working in two separate systems, which one wins when there is a conflict? No wrong answer to this question exists and the better question to answer is how can the conflicts be avoided? Unless you have a compelling case, the hospital will insist your reps work directly in their system and will not be convinced your receivables management software is sufficient enough to allow otherwise. You can utilize your technology to build this compelling case by addressing the integration scenario previously presented.

Before detailing a seamless integration between systems, there are a few prerequisites. Ideally, the following statements are all true:

  • Both the agency and the client have technical teams available to implement and test the integration.
  • There are application programming interfaces or web services available for both the source and target systems.
  • The source and the target system include functionality to track data changes and events that occur as users are working.

Consider your collection software as the source. This is the system you are familiar with and the system you prefer to work with. A data push can be developed using system inherent data integration tools or an external data integration tool with connectors to the system database. This push process will need to be constructed in a manner that captures the data level events as they occur and will need to be shared with the hospital’s system. Simultaneously, a pull or retrieve process will need to be in place for the hospital. This process will grab the real-time data events being provided by the agency’s system. After some analytic, transformation, or validation routines are performed, the data from the agency’s system will ideally auto-update in the hospital’s system. Additionally, reverse processes will need to be engineered for the hospital to automatically share real-time data events with the agency’s system. Achieving this sort of real-time integration will allow your representatives to work exclusively in your system while keeping the hospital’s system in sync and vice versa.

Prior to engineering the solution described above, there will need to be a requirements gathering session with your client to identify the data elements that are important to share and are able to be shared between the two systems. A significant takeaway from these sessions needs to be a requirements document. At a minimum, the following should be detailed in this documentation:

  • A very detailed listing of the data fields to exchange.
  • Any business rules related to data transformations, data validations, and data extract/load procedures.
  • Instructions for using any web service technology.

Utilizing existing data standards is almost essential in the healthcare space. These standards are also helpful in achieving real-time system integration. Your agency is likely already using data standards like Health Level Seven (HL7), HIPAA formats, or something EDI related in order to exchange data with clients. Transaction and code standards like these provide a uniform method for sending and receiving healthcare related data in near real-time. It is important to understand these standards not only because your clients will expect it but also because it displays a higher level of sophistication regarding your operation. The client’s only expectation is you are staying up to date with the ever-changing healthcare standards. The October 1, 2015 countdown to ICD-10 (10th revision of the International Statistical Classification of Diseases and Related Health Problems – Clinical Modification/Procedure Coding System) is on the horizon. Are you ready?

* This article is also published by Collection Advisor

Launching Your Data Warehouse for Skip Tracing

The production data you receive can be simply classified into two categories. It is good data or it is bad data. We refer to the bad data as “garbage data” because it breaks your automated processes, will not load into your software, and in most cases requires some agonizing manual intervention. However, it is important to intelligently collect all skip tracing data and build automated processes for managing it. Technology solutions should be in place to manage your skip tracing data and to avoid your agency being buried in a landfill of skip tracing data.

Although, most collection software includes some sort of inherent module, feature, or perhaps even integrations for interfacing with skip trace service providers, a data warehouse should be implemented as a paired solution for housing and evaluating your skip tracing data. Your agency may already have a data warehouse that can be enhanced to include skip tracing data. Here are some considerations when launching or enhancing a data warehouse for your skip tracing data.

Basic Considerations

The basic, or not-that-exciting, considerations include:
1) Location – You need a server. If your security, compliance, and client requirements do not force you to host the data in-house, I recommend outsourcing to a hosting company. Most hosting companies can dedicate a physical server in a secure environment or implement a virtual environment with the same levels of safety and security.

2) System Sizing – Aside from the other data structures in your warehouse, how much disk space or how much processing power will you need to account for your skip tracing data? The disk space should be enough to house the production data from all systems feeding the data warehouse and from all the ancillary sources, such as your skip trace vendors. Regarding performance, there should be enough muscle to execute all jobs, rules, and interfaces in enough time to not interfere with any regu lar backups. If utilizing a hosting company, they can help you figure this out.

3) Software and Licensing – For most agencies, Microsoft Windows Server and Microsoft SQL Server will do the job. You may require .NET framework, Java, or special drivers based on your preferred technology for interfacing with the data warehouse. This is another area your hosting company can assist with.

Complex Considerations

The complex, and more stimulating, considerations include:
1) Data Structure – All skip tracing data should be captured and stored in your data warehouse even if it is not utilized today. After all, you are paying for it. It is safer to have the data and not need it than to need the data and not have it readily available. Try to construct an intuitive data structure. This will make future build out and scalability much easier.

2) Business Rules – There are many considerations on this topic but let’s focus on the skip tracing data your agency deems important. If you do not have documented business rules stating which pieces of returned data are imperative and how that data is used, you should. Let’s look at a simple business rule example. Most agencies run a skip for phone and address. Of course this information is imperative but how are you handling this returned data? Is it accepted as the best and always overwrites the primary phone and address in your system? Are you running a process to validate the returned phone and address prior to overwriting data in your system? Are you handling the returned data differently based on the source? These are some of the questions your documented business rules should answer. Finally, program these rules into your data warehouse so the process is fully automated.

3) Data Analytics – Many agencies overlook the importance of data analytics and thus lack the understanding of data trends. If you are going through the process of implementing or enhancing your data warehouse to include skip tracing data, you should absolutely run systemic analysis to understand your skip tracing data. This is more than identifying which skip tracing data points are important. Rather, it will show you what the data means. With proper build out, you will understand where you are getting the best skip trace results, what is not working within your business rules, and will likely identify cost saving opportunities.

* This article is also published by Collection Advisor

The Big Data & Integration Summit was a Success

he Big Data & Integration Summit was a success and our presentations are now available to the public for viewing. http://ow.ly/q64hz