The Case for Data Driven Decision Making

Michael CochrumFounder/CDOCMRG/CUBI.Pro

Data Literacy IssuesData Access IssuesCultural IssuesDescriptive Analytics

Defined MeasuresSelf Service AnalyticsData-Driven DecisionsPredictive Analytics

Machine LearningArtificial IntelligenceAutomationPrescriptive Analytics

95%

4%

1%The Data Journey

• The Data-Driven Credit Union uses Data to Strengthen Decisions.• Incentivizes and holds people accountable with data• test assumptions, doesn’t accept gut instinct as the final

answer• Insists on transparency

• The Data Driven Credit Union Increases Ability

• Ties action to outcomes: Do X and Y will happen.• Able to Collaborate and gain Consensus.• Encourages training and learning to enhance ability• Insists on consistency. Do it every time, all the time.

Without data, you’re just another person with an opinion.

W. Edwards Deming

Why Intuition Fails• We are inconsistent• We remember things that

didn’t happen• We are not as good as we

think we are• We won’t give up bad data• We anchor on irrelevant or

over-weighted data• We get hungry and tired

Daniel Kahneman, Psychologist

Why Intuition Fails• We are inconsistent• We remember things that

didn’t happen• We are not as good as we

think we are• We won’t give up bad data• We anchor on irrelevant or

over-weighted data• We get hungry and tired

Daniel Kahneman, Psychologist

COGNITIVE BIAS

Top 5 Cognitive Biases

• Availability Heuristic (this just happened)

• Salience Error (fear factor)

• Ostrich Effect (that can’t happen)

• Outcome Bias (that’s never happened)

• Confirmation Bias (that’s what I thought would happen)

A BCredit Score 720 670

LTV 120% 80%DTI 43% 30%PTI 15% 7%

Net Yield 1.53% 1.62%

Five FAQ’s That Data Can Answer

• How can we generate more loans?• How can we accurately predict loan

performance?• How can we price our loans

competitively and be profitable?

• How can I manage portfolio risk?

• How can I automate more of the lending process?

• Are there risks you are not willing to take?• Have you stopped doing some loans

because they didn’t perform well?• Is it too difficult to qualify for a loan at

your credit union?• Can you retain the loans you already

have?

Case Study #1

$0.00

$1,000.00

$2,000.00

$3,000.00

$4,000.00

$5,000.00

$6,000.00

12 to 23Months

24 to 35Months

36 to 47Months

48 to 59Months

60 to 71Months

72 to 83Months

84 to 95Months

Thou

sand

s

Charge-Off by Term

All

• Curtailed I/L at end of 2017.• CEO: We took significant

losses on 84 mo. loans.• CUBI.Pro: Across the board?

In every tranche?• CEO: Yes, I believe across the

board.

• CUBI.Pro: Can we test that?

We had to pull out of indirect auto lending.

1 2 3 4 5 6 760 to 71 Months 0.48% 0.70% 0.80% 0.94% 0.99% 1.10% 1.10%72 to 83 Months 1.46% 2.27% 2.68% 3.32% 3.57% 3.68% 3.68%84 to 95 Months 1.03% 2.17% 2.46% 3.14% 3.63% 3.84% 3.84%

0.00%0.50%1.00%1.50%2.00%2.50%3.00%3.50%4.00%4.50%

Cum

ulat

ive

Loss

Rat

io2013 Static Pool

1 2 3 4 5 660 to 71 Months 0.87% 1.55% 2.46% 3.17% 3.17% 3.17%72 to 83 Months 1.87% 3.58% 5.13% 6.15% 6.61% 6.61%84 to 95 Months 1.23% 2.96% 5.15% 6.35% 6.79% 6.79%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

Cum

ulat

ive

Loss

Rat

io

2014 Static Pool

1 2 3 4 560 to 71 Months 0.62% 0.64% 1.22% 1.23% 1.23%72 to 83 Months 0.52% 2.32% 4.34% 4.79% 4.79%84 to 95 Months 0.58% 2.67% 4.39% 5.03% 5.03%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

Cum

ulat

ive

Loss

Rat

io

2015 Static Pool

1 2 360 to 71 Months 0.90% 2.19% 2.19%72 to 83 Months 0.38% 1.56% 1.56%84 to 95 Months 0.25% 1.04% 1.10%

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

Cum

ulat

ive

Loss

Rat

io

2017 Static Pool

Average

Balance

Charge-Off

Amount

Annualized

Loss Ratio

60 to 71 Months $1,317,849 $1,102 1.00%

72 to 83 Months $4,208,428 $9,731 2.77%

84 to 95 Months $2,606,817 $5,350 2.46%

Average

Balance

Charge-Off

Amount

Annualized

Loss Ratio

60 to 71 Months $2,004,095 $3,108 1.86%

72 to 83 Months $7,130,448 $18,233 3.07%

84 to 95 Months $10,471,623 $23,278 2.67%

2013

2014

2015

2016

Average

Balance

Charge-Off

Amount

Annualized

Loss Ratio

60 to 71 Months $1,273,885 $777 0.73%

72 to 83 Months $6,436,726 $11,845 2.21%

84 to 95 Months $12,907,965 $23,354 2.17%

Average Balance

Charge-Off

Amount

Annualized

Loss Ratio

60 to 71 Months $1,258,056 $654 0.62%

72 to 83 Months $5,111,771 $8,199 1.92%

84 to 95 Months $16,952,852 $24,011 1.70%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

0 Years 1 Year 2 Years 3 Years 4 Years 5 Years 6 Years

Cumulative Loss Ratio By Tier

A+ A B C D

2019 2018 2017 2016 2015 2014 2013A+ $686,064.06 $3,372,064.15 $5,194,432.54 $5,239,568.27 $7,197,999.26 $6,503,241.47 $6,155,970.76A $970,016.61 $4,572,970.01 $10,612,443.92 $10,550,646.51 $10,551,372.55 $10,384,376.02 $8,682,143.21B $426,710.29 $1,676,143.98 $10,298,076.47 $10,611,587.06 $11,236,500.15 $12,055,915.45 $8,695,848.27C $109,823.00 $524,174.72 $3,586,969.13 $5,023,655.06 $5,004,262.67 $7,305,382.56 $5,049,130.64D $84,657.00 $505,601.81 $835,131.80 $1,380,178.90 $1,619,704.16 $882,005.38

$0.00

$2,000,000.00

$4,000,000.00

$6,000,000.00

$8,000,000.00

$10,000,000.00

$12,000,000.00

$14,000,000.00O

rigin

atio

n Am

ount

Originations by Tier (72 & 84 Mo. Term)

A+ A B C D Unknown60 to 71 Months 2.62% 3.22% 5.92% 7.48% 12.74% 3.00%72 to 83 Months 2.97% 3.75% 5.92% 7.92% 10.48% 4.73%84 to 95 Months 3.06% 4.12% 5.66% 6.92% 7.20% 4.35%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

rate

Average Rate by Tier

Summary of Analysis• Credit Union Indirect loan performance was

poor, especially on loans with 72 – 84 month terms.

• Poor performance, however, was predominately B, C and D borrowers.

• Credit union could have more precisely priced for risk.

• Cutting deep over-corrected and limited growth potential.

• Are there risks you are not willing to take?• Have you stopped doing some loans

because they didn’t perform well?• Is it too difficult to qualify for a loan at

your credit union?• Can you retain the loans you already

have?

Case Study #2

• CEO: Our CFO and our Planning Consultant disagree over which loan type is more profitable.

• CUBI.Pro: This is probably because they are using different measurements. Can we take a look?

Is Direct Lending more profitable than Indirect?

All OriginationsDirect Indirect

2017 3.44% 2.03%2018 3.69% 2.78%

2013 – 2017 Auto Originations

2017-18 Originations OnlyDirect Indirect

2017 2.46% 4.13%2018 2.52% 3.11%

2016 Origination Pool - 27 Months

90-100% LTV

Tier Avg Balance Annualized Income Total Losses Avg Annual

YieldAnnualized Loss Ratio

Annualized Net Yield

A+ $825,333 $30,900 $0 3.74% 0.00% 3.74%A $828,701 $37,253 $11,643 4.50% 0.43% 4.06%B $504,762 $31,625 $3,621 6.27% 0.22% 6.04%C $392,361 $31,282 $29,033 7.97% 2.28% 5.70%D $123,708 $9,994 $17,599 8.08% 4.38% 3.70%

100-120% LTV

Tier Avg Balance Annualized Income Total Losses Avg Annual

YieldAnnualized Loss Ratio

Annualized Net Yield

A+ $1,485,871 $44,474 $0 2.99% 0.00% 2.99%A $3,066,378 $118,507 $85,118 3.86% 0.85% 3.01%B $1,352,809 $67,627 $362,655 5.00% 8.25% -3.25%C $438,418 $24,914 $264,770 5.68% 18.58% -12.90%D $135,601 $9,502 $39,037 7.01% 8.86% -1.85%

>120% LTV

Tier Avg Balance Annualized Income Total Losses Avg Annual

YieldAnnualized Loss Ratio

Annualized Net Yield

A+ $1,056,055 $31,971 $83,917 3.03% 2.45% 0.58%A $2,748,719 $110,019 $150,092 4.00% 1.68% 2.32%B $4,400,133 $248,382 $64,437 5.64% 0.45% 5.19%C $2,204,896 $152,300 $24,665 6.91% 0.34% 6.56%D $221,938 $19,300 $35,140 8.70% 4.87% 3.82%

2017 Origination Pool - 27 Months

90-100% LTV

Tier Avg Balance

Annualized Income

Total Losses

Avg Annual Yield

Annualized Loss Ratio

Annualized Net Yield

A+ $988,768 $32,387 $0 3.28% 0.00% 3.28%A $1,108,626 $45,795 $0 4.13% 0.00% 4.13%B $901,230 $53,511 $8,989 5.94% 0.44% 5.49%C $280,582 $22,158 $0 7.90% 0.00% 7.90%D $40,871 $4,966 $0 12.15% 0.00% 12.15%

100-120% LTV

Tier Avg Balance

Annualized Income

Total Losses

Avg Annual Yield

Annualized Loss Ratio

Annualized Net Yield

A+ $1,561,829 $52,756 $569 3.38% 0.02% 3.36%A $3,993,865 $155,464 $87,430 3.89% 0.97% 2.92%B $4,626,373 $253,716 $85,883 5.48% 0.83% 4.66%C $1,943,510 $132,280 $42,390 6.81% 0.97% 5.84%D $257,193 $20,955 $8,316 8.15% 1.44% 6.71%

>120% LTV

Tier Avg Balance

Annualized Income

Total Losses

Avg Annual Yield

Annualized Loss Ratio

Annualized Net Yield

A+ $1,233,052 $42,285 $43,498 3.43% 1.57% 1.86%A $2,628,751 $107,617 $73,194 4.09% 1.24% 2.86%B $2,153,677 $115,466 $42,388 5.36% 0.87% 4.49%C $451,222 $27,083 $24,329 6.00% 2.40% 3.61%D $60,591 $4,585 $0 7.57% 0.00% 7.57%

Summary of Analysis

• “Profitability” can appear to be different, depending upon what measure and period you use to measure.

• Isolation of product characteristics enables one to see key difference in performance that are not explainable by the product characteristics.

• Granular analysis allows lenders to more accurately price loans.

Case Study #3

• CLO: Our CFO keeps saying that some products are not profitable. His reports are compelling, but I’m not convinced.

• CUBI.Pro: Likely, your CFO is calculating profitability during a particular period. This can yield unexpected results.

Granular Product Profitability

Loan GroupAvg Monthly

BalanceWAR COF

Dealer

Fee %OE

Avg

Loss %

Est. Avg

Net Yield

Direct Auto $6,691,845.72 4.29% 0.22% 0.00% 2.10% 0.27% 1.70%

Indirect New Auto $35,276,061.12 2.80% 0.22% 0.82% 1.10% 0.41% 0.25%

Indirect Used Auto $22,611,220.70 3.59% 0.22% 0.89% 1.10% 0.77% 0.61%

Mortgage $55,587,281.07 3.76% 0.22% 0.00% 2.10% 0.00% 1.44%

Other $61,054,848.49 4.36% 0.22% 0.00% 2.10% 0.26% 1.79%

Signature $4,932,216.26 10.91% 0.22% 0.00% 2.10% 2.16% 6.43%

Tier Loan GroupAvg Monthly

BalanceWAR COF

Dealer

Fee %OE

Avg

Loss %

Est. Avg

Net

Yield

Risk Tier 1 Direct Auto $2,519,017.53 2.98% 0.22% 0.00% 2.10% 0.00% 0.66%

Risk Tier 1 Indirect New Auto$23,121,474.4

22.20% 0.22% 0.76% 1.10% 0.16% -0.04%

Risk Tier 1Indirect Used

Auto

$11,141,725.3

02.32% 0.22% 0.84% 1.10% 0.15% 0.01%

Risk Tier 1 Signature $1,608,813.63 9.38% 0.22% 0.00% 2.10% 0.93% 6.12%

Tier Loan GroupAvg Monthly

BalanceWAR COF

Dealer

Fee %OE

Avg

Loss %

Est. Avg

Net Yield

Risk Tier 3 Direct Auto $1,489,783.02 5.20% 0.22% 0.00% 2.10% 0.36% 2.51%

Risk Tier 3Indirect New

Auto$3,054,282.79 5.19% 0.22% 0.67% 1.10% 1.76% 1.43%

Risk Tier 3Indirect Used

Auto$3,389,516.26 5.44% 0.22% 1.03% 1.10% 1.39% 1.70%

Risk Tier 3 Signature $870,855.34 12.69% 0.22% 0.00% 2.10% 1.93% 8.45%

Summary of Analysis

• Product profitability is more precisely calculated using lifetime cashflows rather than period calculations.

• Drilling-down into data can provide information as to what risk factors are more impactful to product profitability.

• Providing this level of information help supports decision of lenders to increase risk.

Case Study #4

• CEO: We are trying to remain competitive, but we just don’t seem to be making money on our loans. How can other lenders do it?

• CUBI.Pro: How do you know they are? Do you know that the rates published are the rates they are charging?

Default Probabilities by Risk Factor

Origination

Count

Predicted 90-

Day Default

Count

Predicted 90-

Day Default Rate

Actual 90-Day

Default Count

Actual 90-Day

Default Rate

Tier 1

101-110% 557 4 0.68% 9 1.62%

111-120% 428 3 0.68% 17 3.97%

> 120% 9 0 0.68% 2 22.22%

Tier 2

101-110% 485 20 4.08% 25 5.15%

111-120% 177 7 4.08% 14 7.91%

> 120% 8 0 4.08% 1 12.50%

Tier 3

101-110% 499 43 8.60% 54 10.82%

111-120% 106 9 8.60% 9 8.49%

> 120% 4 0 8.60% 1 25.00%

Tier 4

101-110% 109 12 11.37% 19 17.43%

111-120% 24 3 11.37% 4 16.67%

> 120% 1 0 11.37% 1 100.00%

Tier 5

101-110% 62 9 14.52% 14 22.58%

111-120% 18 3 14.52% 3 16.67%

> 120% 14.52%

Tier 6

101-110% 62 22 36.20% 18 29.03%

111-120% 14 5 36.20% 4 28.57%

> 120% 2 1 36.20% 0 0.00%

Origination

Count

Predicted

90-Day

Default

Count

Predicted 90-

Day Default

Rate

Actual 90-

Day Default

Count

Actual 90-

Day Default

Rate

Tier 1

No 1432 10 0.68% 24 1.68%

Yes 1289 9 0.68% 14 1.09%

Tier 2

No 880 36 4.08% 36 4.09%

Yes 630 26 4.08% 15 2.38%

Summary of Analysis• The credit union should be continually

measuring performance against predictive values.

• It is important to understand the different between the risk of default and the risk of loss.

• It is important that the credit union is accurately pricing loans based relative risk factors, not simply pricing categories based on credit score and term.

Other Areas To Review• Stress-Testing Risk Concentrations• Updating risk attributes to re-risk grade

your portfolio.• Consider dynamic pricing models

based on your CECL models.• Use data to improve efficiencies in

lending by throwing out policies and guidelines that have little impact on loan performance.

The Data-Driven Credit Union

Creating a Data-Driven DecisionCulture in Your Credit Union

Thank You!

Michael Cochrum, CEOCUBI.ProP: 972.814.1477E: michael.Cochrum@cubi.proW: www.cubi.proFollow Me on LinkedIn!