Collection Optimization using Analytics

16th October 2016

V1.0

Impact of Operation Decision:More contracts assigned to collectors.

Impact of Current Economic situation: 2016 has witnessed Overdue % all time high.

Challenges faced by ALJUF Operation

July August September October In Near Future0

100

200

300

400

500

600

700

800

900

# Contracts Assigned to Collector (Ahmed Elserafy: Amir Soultan Rd)

May June July August September0

0.20.40.60.8

11.21.41.61.8

Overdue %(Active Contracts)

2014

% O

verD

ue

Need to increase overall ability to collect the most due for the least cost.

43%

12%

45%

Advance Ontime Overdue

Reason for Rising Overdue *

Distribution of Guestswith respect to their Payment Behavior.

* Sample is taken from 2 collectors in Amir Sultan Road Branch, having 847 Guests (having 869 contracts) studied in August 2016.

Overdue increases because the segment of overdue customers increases at the cost of either Advance Customers or Ontime Customers.

Each segments has its own characteristics and require differential treatment with the main objectives:-i. Avoid increasing of Overdueii. Avoid alienating of profitable customers.

Majority of Advance Guests pays without any treatment, some requires SMS reminder, fewer requires call treatment, and care must be taken to handle these Guests so that they maintain this positive behavior.

Most “on time” Guests are called by Collector, and/or SMS send to them. Some require a follow up calls.

Most of overdue Guests need to be called, and reminded, and their promise to pay is broken, so follow up and escalation are routine treatment. Some from this segment have no effect on traditional collection treatment.

21%

22%

18%

17%

23%

Payment Week Distribution

0: Didn’t Pay within the month 1: Paid In First Week 2: Paid In Second Week3: Paid In Third Week 4: Paid In Fourth Week

Payment Week Distribution

W1W2

W3

W4 0

Payment week distribution can help in organizing our collection effort if we have a model to predict each Guests’ payment behavior.

Actionable insights from predicted Advance payer behavior.

Strategic Collection Effort helps us to identify advance Guests and are handled accordingly to maintain their payment behavior and avoid alienating these profitable customers.

Treatment of Advance Guests

العزيز  عميلناأو فروعنا من بأي السيارة إيجار بسداد نذكركم

سجلكم جودة على حفاظاً سداد خدمة طريق عناالئتماني

للتمويل عبداللطيفجميل

(ii) After waiting for 2/3 days time to react on SMS, second SMS as reminder.(iii) After waiting for 2/3 days time to react on SMS, if still not paid, collector calls to remind.

At end of each week, we check Guest’s payment status whose maturity time to pay within that week has arrived. If not paid then (i) send SMS.

Actionable insights from predicted Due Guest’s payment behavior.

Strategic Collection Effort helps us to identify and intensify collection efforts early on, and call Guests only when necessary: Optimize calling effort.

العزيز  عميلنامن بأي المستحق السيارة إيجار سداد نأملسرعة

جودة على حفاظاً سداد خدمة طريق عن أو فروعنااالئتماني سجلكم

للتمويل عبداللطيفجميل

Right Guest to call, at the Right Time, with Right Intensity.

• Priority list of calling: Overdue Customers from previous month. Guests are treated by calls coupled with SMS:-

• At end of each week, we check Guest’s payment status whose maturity time to pay within that week has arrived. If not paid then initiate collection effort.

• Guest are ranked in the calling list according to their paying behavior so that the collection agent can adapt their call tone/message.

If Guests are repeated delinquent, then following SMS is used instead of wasted calls, and delegate to legal.

Actionable insights from predicted Overdue Guest’s payment behavior.

Strategic Collection Effort helps us to identify fruitless calls and replace with other effective treatment strategy like starting with SMS and followed by actual legal handling.

المحترم عميلناأمام لمطالبتكم القانونية للشؤون ملفكم بتحويل نفيدكم

منكم الموقعة ألمر بالرياضبموجبسندات التنفيذ محكمةللتمويل عبداللطيفجميل

Week1 onwards

Treatment of Potential Overdue GuestsSince most Guest pay their due in later part of the month, the first week is utilized by collectors to call those who are predicted to default within the month window ( potential overdue).

• Guest likelihood to “survive” steadily decreases, till it reaches 0, implying, the Guest should have paid by then.

• Potential Delinquent customers’ probability to “survive” will not reach 0, within an identified window (beyond which Guest is Overdue).

Survival Analytics to predict Guest payment behavior.

Survival Model helps us to predicts:- i. Which Guest will pay; ii. When will they pay; iii. Ranking of bad defaulter

Footprints of Customer Data

Guest MaritalStatusId

NationalityId

WorkPlaceId JobId

GuestGovernmentId Age

NoOfDependents Income

GuestGenderId

NoOfContracts

Opening DueSAR

OpeningAdvance

U/C* Predicted

1008783530 1 1 600 130 2 38 0 19000 1 1…. ….

0.854 0

How two extreme Guests are predicted( Defaulter):-Best rated Guest in August 2016.

*U/C=Used Period/Contract Period

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35

4 4 4 3 4 4 4 4 3 3 0 2 3 3 3 2 3 2 2 2 1 2 1 1 1 1 1 0 0 0 0 0 0 0 0

Used Period

Paid in Week

Guest MaritalStatusId

NationalityId

WorkPlaceId JobId

GuestGovernmentId Age

NoOfDependents Income

GuestGenderId

NoOfContracts

Opening DueSAR

OpeningAdvance

U/C* Predicted

2344026428 1 40 430 105 1 52 0 50000 1 1…. ….

0.812 1

How two extreme Guests are predicted( Week 1 Payer):-Best rated Guest in August 2016.

*U/C=Used Period/Contract Period

Used Period

Paid in Week

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

1 0 4 1 3 4 4 2 2 2 1 1 0 4 1 3 1 4 1 4 1 1 4 4 1 3 1 1 1 1 1 1 1 2 1 1 1 1 1

Results Oct 2015.

661 Guests Total

Paid 508 19 527

Not Paid 40 94 134

Total 548 113 661

Pay Not PayActualPredicted

Model Accuracy= (508+94)/661=

Model Misclassification Rate= (40+19)/661=

96.39%Paid Prediction Rate=508/527=

91.07%

70.15%

8.92%

Unpaid Prediction Rate=94/134=

Total Due reported = SAR 159,517Average Due of Predicted cases who indeed did not pay = SAR 1,697.Assuming 30% chances of recovery from these cases, Overdue recovery= 0.3*1697*94=47,855Overdue reduction = 45,855 SAR

Month Year Week1 Week2 Week3 Week4 till end DefaulterOctober 2015 66.26% 69.14% 85.02% 59.91% 91.07%November 2015 84.37% 66.28% 73.08% 73.95% 93.78%December 2015 75.64% 67.28% 65.16% 75.21% 93.48%January 2016 62.92% 79.25% 88.35% 73.49% 91.57%February 2016 61.51% 78.80% 87.91% 76.20% 81.14%March 2016 60.12% 69.19% 89.56% 76.89% 70.81%April 2016 60.62% 70.62% 88.12% 80.75% 92.62%May 2016 60.12% 69.19% 89.56% 76.89% 70.80%June 2016 69.86% 63.59% 79.55% 80.85% 87.94%July 2016 64.50% 83.25% 86.20% 77.95% 86.44%August 2016 68.08% 64.43% 78.33% 71.73% 89.28%September 2016 75.21% 74.15% 84.61% 68.74% 70.86%October 2016 72.59% 88.08% 89.51%

Collection Optimization Model Accuracy Results.

The accuracy matters most in the defaulter prediction, where average accuracy is 85%.Weekly Prediction helps in load balancing calls for collectors.

Month Year

Paid Prediction Rate

UnPaid Prediction Rate Quality

October 2015 96.39% 70.15%

November 2015 97.61% 79.45%

December 2015 98.59% 72.46%

January 2016 97.27% 75.63%

February 2016 77.80% 92.94%

March 2016 93.12% 71.64%

April 2016 97.83% 70.39%

May 2016 85.05% 22.40%*

June 2016 89.91% 81.68%

July 2016 85.65% 87.40%

August 2016 92.15% 10.38%*

September 2016 62.95% 89.96%

Prediction Accuracy Affecting Revenue from Collection Effort

The blips in Unpaid Prediction rate in May’16 and Aug’16 is explained by the sudden change in trend of overdue. However as the trend restores, the accuracy is restored.

1. This results is under testing at Amir Soultan Branch with two collectors level for November.

2. Starting December’2016, the solution should be extended to the entire Amir Soultan Branch to halt the rising overdue.

Above steps can be implemented with “0” cost.3. After gaining the confidence and improvement in the model, the scope

will be extended to allJan’2017 onwards this solution will be extended to sub-region with 1 SAS license and 1 server, dispatching weekly lists to each collector.

4. Mar’2017, after review of result, SAS consultants will be required to architect this solution for entire region. License cost will remain same, server cost will increase.

Way Forward:-