recovery optimization

Op#mizing Recoveries for Loan Servicing Por6olios Through Smart Alloca#on

Using Data Analy#cs & Cri#cal Thinking to Beat Your Compe#tors

S. Blair Korschun August 10, 2011

How should we allocate a post charge-‐off consumer loan por6olio to op#mize servicing results?

First we need to ask the right ques#ons.

The typical ques#ons asked include:

What are my current alloca#on op#ons?

What are the recovery rates for each servicing op#on? For example a 6 month batch liquida#on rate would be the percentage of the face value of debt owed that is recovered within a 6 month period with the batch being the por6olio debt placed during a single month (the batch).

Another important ques#on is – What is the cost to achieve the recovery rate for each servicing op#on?

2 Using Data Analy#cs & Cri#cal Thinking to Beat Your Compe#tors

What are our current alloca#on op#ons?

For simplicity let’s assume that we are considering only fresh charge-‐off accounts and that we’ve been using one internal team and two outside recovery agencies. Assume we’ve given 40% to internal team and 30% shares each to the two agencies.

Are these the only op#ons we have without adding people or a new outside agency?

What about a no work strategy where you only respond to inbound calls? Yes there would be low results but there would also be very low cost.

What about using an automated le_er only strategy (no outbound calling)? Again this would be rela#vely low cost but higher than the no work strategy op#on.


If we know the liquida#on rate then how should we allocate?

Let’s ignore the no work and le_er only op#ons and only consider the internal team and two agencies.

How should we allocate the accounts?

Should we give more to Agency A?

Should we fire Agency B?


Team 6Mth Liq. Rate

Internal 8.04%

Agency A 9.35%

Agency B 7.12%

What other ques#ons should we ask and answer before changing our alloca#on strategy?

We need to understand cost as the net recovery rate is more important than the gross. Let’s assume we know the costs as a percentage of recovery dollars.

It now appears that Internal is doing the almost as well as Agency A.

Should we give more to Internal and Agency A and fire Agency B?


Team 6Mth Liq. Rate

Cost Net Liq. Rate

Internal 8.04% 21% 6.35%

Agency A 9.35% 30% 6.55%

Agency B 7.12% 30% 4.99%

We s#ll need to ask more ques#ons.

We should ask if the sample size is sufficient to give us confidence that the differences are sta#s#cally significant. We should also understand if the results fit a normal distribu#on curve.

In our example of recovery performance the results with a batch are not normal as you would have a fat tail due to non-‐payers (i.e. lots of accounts with value of zero). On the other hand if we have 3 years of monthly results then the results per batch may fit a normal curve – but we don’t want to wait three years to gather data to verify its significance.

It is fairly common to test hypotheses using confidence intervals. Using 95% confidence is typical but you can adjust this. Read more about this is any Sta#s#cs textbook or see the CONFIDENCE func#on in Excel.

Check with a sta#s#cian or do the math yourself to ensure that you can have confidence that the performance differences are real (significant).


Having consistent trends in our data is cri#cal to being able to act with confidence.

We also need to know if the results are changing over #me.

Scenario A – Inconsistent results over #me

Scenario B – Consistent results over #me

We want to see data that is consistent over #me as in scenario B. Having data like scenario B allows us to build strategies around the results to improve performance.


Team Jan Net Liq % Feb Net Liq% Mar Net Liq%

Internal 8.31% 5.65% 6.35%

Agency A 5.65% 7.78% 6.55%

Agency B 7.58% 3.69% 4.99%

Team Jan Net Liq % Feb Net Liq% Mar Net Liq%

Internal 6.31% 6.65% 6.35%

Agency A 6.65% 6.78% 6.55%

Agency B 5.18% 4.89% 4.99%

For now ignore the #me series results and assume results are stable. What other ques#ons must we ask?

Are there segments within the por6olio for which the liquida#on results vary substan#ally from the team average result?

Let’s look at one example with two segments – good phone # and bad phone # (oken called “skips”).

Now, how should we allocate accounts?

Are you s#ll thinking that firing Agency B might make sense?


Team Good # Net Liq %

Bad # Net Liq %

Internal 11.22% 2.15%

Agency A 10.39% 2.55%

Agency B 7.50% 4.13%

How should we find meaningful segments to consider in our alloca#on strategy?

We need to ask what data do we have? What variables can we measure and analyze?

Typical variables to consider would include balance size, credit limit, interest rate, days since account opened, days since last payment, credit score, cash advances, etc.

One strategy is to take all data fields available and run all of them through one or more modeling techniques (Regression, Chiad, Cluster analysis, etc) to find meaningful varia#on in results by score band or by segmenta#on/cluster.

If we test all known data fields that we have, then are we done? Is there more that we can or should do?


There is more data to be had if you ask the right people. Who should we ask?

One error that sta#s#cians and analysts make is not talking enough to the people on the front lines.

We should ask our internal collectors what factors seem to ma_er in who pays and who doesn’t. We should ask our agency vendors what factors they consider as important. We should ask the Opera#on managers and supervisors for their input.

They may tell you things like: -‐ several states have non garnishment laws – i.e. create a cluster for that -‐ u#liza#on ma_ers (balance divided by credit limit) – i.e. create a transforma#on variable from two others -‐ first payment defaults ma_er (never made a payment) -‐ someone who made many small payments before defaul#ng is likely to pay

You should get lots of ideas to create new variables or clusters.


Aker speaking to our Ops people and vendors and crea#ng transforma#on variables is there s#ll more data to obtain?

Oken financial companies have different systems of record for origina#ons and for servicing. So you might obtain more data if you can study the origina#on data as well.

At a cost you can also obtain poten#ally important data from outside sources. Most common sources include the major credit bureaus which can supply data on:

-‐ Are they paying other bills on #me or at all? -‐ How many other debts are delinquent or charged-‐off?

-‐ How much total debt and total credit do they have?

-‐ Do they have a mortgage or auto loans?


Assume we’ve created the best net recovery scoring/ segmenta#on model possible. Now, how should we allocate the por6olio?

Assume we se_led on four segments as follows and that sample sizes/ confidence intervals are good and #me series results appear stable.

Firing Agency B now appears to be a mistake, but what should we do?

We could give 100% of each segment to the best performer as circled above.

Would this be smart?


Team Segment A Segment B Segment C Segment D

Internal 9.69% 8.44% 5.04% 2.09% Agency A 11.35% 6.97% 5.18% 1.79% Agency B 6.20% 4.54% 5.57% 3.34%

We should ask are there logical constraints to consider in op#mizing our alloca#on strategy.

Some logical constraints could include:

-‐ Corporate, Risk or Legal considera#ons including requirements to always have two or more vendors or possibly limi#ng share to no more than 70% to any single vendor.

-‐ We may need to keep X number of internal employees which would require a minimum account volume. Likewise there may be a hiring limit or freeze which could limit new volume placements

-‐ We should keep a minimum alloca#on of each segment to each vendor to watch for result trend changes over #me which do occur

-‐ Some vendors might have capacity limits and their results may fall if given too many addi#onal accounts too quickly


Maximizing net recoveries across segments with many alloca#on requirements / constraints may be best solved with linear programming.

Our goal objec#ve would be to maximize net recovery dollars.

Assume we have 10,000 accounts per month to allocate.

Constraints might include items like:

-‐ Internal min = 2,000 and max = 5,000 with a change of no more than X% per month

-‐ Each Agency’s share must be >=10% and <=70%; Agency share can’t change more than +/-‐ 1,000 per month

-‐ Agency A has an upper capacity limit of 4,000

-‐ Each team must get at least 100 accounts per segment per month

You could write a simple Linear Programming Model to solve / op#mize this problem using “SOLVER” in Excel or choose from many other programs .


What else should we consider?

Make sure that before your models / segmenta#ons are finalized that other departments have signed off. For example Legal/Risk would likely not let you use full Zip code as a variable as it could be considered red lining.

Also confirm with Opera#ons, Vendor Management and HR what you are planning. Sudden volume shiks are likely to hurt results and hiring/training may take #me. Opera#ons likes to have predictable volumes.

Also consider the difficulty and cost of gexng certain data. Maybe you can get 90% of the model’s power from using only three variables. If true, then do you really need 12 variables in your model?

Also it is very important to publish and share data results and to step the ground rules for Internal and the agencies. Performance has a way of improving quickly when measured and reported publicly.


Let’s review our current strategy’s results.

Our original alloca#on was 40% to Internal and 30% each to our two agencies. We will assume alloca#on was consistent in share across our four defined segments. We will assume we have 10,000 accounts per monthly batch.

This original distribu#on with our liquida#on results from page 12 predicts a monthly batch net recovery of $2,123,206.


Segment A Segment B Segment C Segment D Total

Internal 1,256 740 944 1,060 4,000

Agency A 942 555 708 795 3,000

Agency B 942 555 708 795 3,000

Total 3,140 1,850 2,360 2,650 10,000

Avg. Bal $ $3,250 $5,105 $2,841 $3,088 $3,454

Let’s review our LP Model constraints.

•  Sum of all segments = 10,000 •  Sum of alloca#on to Internal + Agency A + Agency B = 10,000

•  Sum of segment A distribu#on = total of segment A

•  Sum of segment B distribu#on = total of segment B

•  Sum of segment C distribu#on = total of segment C

•  Sum of segment D distribu#on = total of segment D

•  Internal must have at least 2000 accounts

•  Internal can't have more than 5000 accts

•  Agency A must have >=10% share •  Agency A must have <=70% share •  Agency B must have >=10% share


•  Agency B must have <=70% share •  Agency A has a capacity limit of 4000 •  All solved values must be integers

•  Internal Segment A >= 100

•  Internal Segment B >= 100

•  Internal Segment C >= 100

•  Internal Segment D >= 100 •  Agency A Segment A >= 100

•  Agency A Segment B >= 100

•  Agency A Segment C >= 100

•  Agency A Segment D >= 100

•  Agency B Segment A >= 100

•  Agency B Segment B >= 100

•  Agency B Segment C >= 100

•  Agency B Segment D >= 100

Now let’s see our op#mized LP Model results.

We assume the same popula#on and distribu#on of segments solved to maximize net recovery subject to the constraints on the prior page.

This op#mized distribu#on with our liquida#on results from page 12 predicts a monthly batch net recovery of $2,540,549. This predicts a lik of $417K per monthly batch or 19.66% or a net annual improvement of $5M per year on a batch basis.


Segment A Segment B Segment C Segment D Total

Internal 100 1650 150 100 2,000

Agency A 2,940 100 100 100 3,240

Agency B 100 100 2,110 2,450 4,760

Total 3,140 1,850 2,360 2,650 10,000

Avg. Bal $ $3,250 $5,105 $2,841 $3,088 $3,454

Are there other constraints to consider?

Yes, this model is only a simple example. There are many other issues to consider including the profitability of the servicing work for both internal and external vendors.

Collec#on/Recovery agencies usually follow the unit yield on their client assigned paper.

Unit Yield = Liquida#on Rate x Average $Balance x Commission %

If the expected/actual unit yield drops significantly the vendor will either be forced to pull resources off of the por6olio or they could actually resign from being a servicer. On the flip side, client por6olios with a high unit yield can demand be_er staffing ra#os and more experienced staff.

Such considera#ons are important when working with agencies and should be reflected as part of any LP Model’s constraints.


Before changing to the new op#mized alloca#on are there other issues to consider?

Yes! There are many issues to think through before making the changes.

•  Internal’s share will be cut in half. This would mean cuxng or realloca#ng half the current internal staff. Are we willing to do this? Should we give our Internal group #me to improve its results?

•  Will Internal’s cost structure change with a large reduc#on in volume?

•  Agency B would receive 58.7% more volume. Can they handle this increase and if so then how quickly?

•  Should we iden#fy to Internal and/or to the Agencies which accounts are which segments so they can work harder on the higher liquida#on accounts? How will we share results?

•  Should we change the Agency commission rate based on segments?

•  How oken should we verify the results and alter the alloca#ons? •  How oken should we rebuild the segmenta#on model?


Conclusions on Op#mizing Recoveries through Smart Alloca#on

Using these smart analy#c techniques could easily improve net recoveries by 10-‐20% or more verses a tradi#onal straight share alloca#on method.

–  Remember to ask lots of ques#ons –  Measure your goal objec#ve (i.e. net recoveries over some batch period) –  Consider cost –  Look for all relevant, usable data to create segmenta#ons –  Talk to Opera#ons and your vendors; talk to Legal, Risk, HR, etc for their input –  Check sample size and significance (Hypothesis tes#ng and Confidence Intervals)

–  Make sure the trend is tracked and is meaningful (i.e. don’t want to see wild swings in performance)

–  Consider the 80/20 rule when building a model / segmenta#on – is it worth the complexity?

–  If there are many constraints, then consider using LP modeling –  Measure and publish /share the results by segment (Shine a light on things)


For More Informa#on:

For more informa#on about this presenta#on you may contact the author at:

[email protected]

LinkedIn: www.linkedin.com/in/blairkorschun


recovery optimization

Documents