Collections Brief #1
What is Skill-based Assignment and How can you make it work?
The performance of your collections operation is highly dependent upon your human capital – the collection agents that speak to your borrowers, understand their situation, and collaboratively work towards the best payment outcome.
measuring accurately / supporting comparison
managing variability (in the difficulty of the call, the value of the outcome, the skill of the agent)
The challenge is to leverage the inherent variability to create the best business outcome. Measurement will be important, as always, but you will sacrifice more complete comparisons to do this and be happy that you did.
Let’s get into it!
It is natural to ask a set of fundamental questions about your collection agents:
Who are my best performing agents?
How much value do collectors add to my organization?
o Best-performing agents / average agents / worst-performing agents
Are my performance incentives aligned to value added?
Do I have the right number of collectors in my portfolio today?
o In principle, you’d like to keep adding until value added matches salary and there is a net zero benefit of the addition
Making these determinations is not as easy as it sounds, so it can be tempting to do a “pure random” or “stratified random” account assignment to each collector for the simplicity of measurement and comparison. An example of “stratified random” might be random assignment within a simple group, such as collection stage (early / late) or geography.
Typically, what you will find when you can compare with random assignment is that the best agents are worth 1.5X your worst agents while market compensation does not scale similarly. This means that most of the net value added (cash collected less cash compensation) is coming from your highest performing agents.
Here, some illustrative numbers would be a normal distribution, where if you had 20 agents handling ~475 accounts each, Group 1 (worst) has 3 collectors, Group 4 (best) also has 3 and the 2 middle groups have 7 each. Example relative performance from worst to best could be 0.80, 0.93, 1.07, 1.20 where 1.00 is the average.
Across the delinquent portfolio, there is a high range of variability in the borrower’s situation:
In early stage, there can be a need for a simple reminder or can be lower risk “sloppy payer” that are 1-2 weeks late many months but never a month late.
There can be a change in financial status (often seen in buckets 2/3) due do a job loss, illness, divorce, or death in the family that requires a detailed re-assessment.
In late buckets, conversations are less frequent but very serious for the borrower (repossession, loss of credit card utility, delinquent payment hierarchy, settlements).
Similarly, there is variability in the value of the outcome:
Curing higher loan balances is much more valuable, there can be $30 K and $1 K balances in the same portfolio
Getting a later-stage delinquent account back on track is worth more than early stage
When the loan is secured, collateral value (or forecast of collateral value) and LTV can mean a loss now vs a profit if 2 payments can be collected in 2 months.
Each situation requires different collector skills:
Early stage “simple reminders” generally don’t require much skill or detailed understanding of the borrower’s situation.
Mid-stage situations can be akin to a loan modification that requires the collector to be a bit of a financial counselor, as there can be detailed eligibility criteria and cooperation required from the borrower that needs to be clearly and simply explained.
Late-stage scenarios need a “tough but fair,” non-judgmental approach that is difficult for many agents.
Your best agents get to focus on where they are strongest and where they add the most value
Measurement works within groups but you give up some visibility across groups
The specifics of the assignment can be score-based or rules based depending on richness of historical data
Because most delinquent loans are moving towards charge-off, you often don’t get a second chance to make the right decision – borrowers get harder to reach as they get more delinquent, and their financial flexibility to work toward a solution often decreases over time. Good decisions here will compound over time, making skill-based assignment very valuable.
In a simple example, rank ordering by amount due and giving the best agents the highest amount due produces an annual lift in cash collected of more than $2.2 MM in a $100 MM delinquent portfolio.
Agents need to be on-board with this change and need to know their new performance expectations, which change as their work queue changes. Skilled agents may see their dollars collected and liquidation rates go down relative to a random work queue assignment, although their value add with go up. Getting this right is a big part of your success.
How to Implement Skill-based Assignment
With the systems you have today, you likely will need to write out a detailed functional specification for which account gets assigned to which collector, taking due care that the assignments are comparable for measurement where they need to be, assignments update at month end or cycle end, and that reporting can keep up.
Once this is done, there are two implementation choices:
1. Manual file handling and queue assignment – high potential for errors.
2. Programmatic implementation – tends to require organizational prioritization, cost benefit assessment, and considerable time to implement, test, and deploy.
We estimate that most organizations would take 2-3 months for a manual approach and 5-6 months for a programmatic implementation.
Please be aware that Lendisoft’s Collection Optimizer can handle Skill-based Assignment from a GUI with a few clicks and would be one day from creation to deployment without any internal IT resources needed.
This 3- to 6- month acceleration would be worth roughly $500,000 to $1 MM on a delinquent portfolio of $100 MM. For a free diagnostic and to learn more, contact us.
Kevin Walsh is a strategic business leader, actively advising high-growth lenders on consumer and commercial credit in the US and in emerging markets. Deliver big bank discipline, start-up scrappiness, and innovative problem solving capabilities.