predictive analytics in consumer lending reducing risk
TRANSCRIPT
Predictive Analytics in Consumer Lending:Managing Portfolio RiskApril 2015
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www.pi-cube.comBorrower Risk Index
• Quantifies a borrower’s likelihood of default.
$
1. Combines financial history and the state of the economy
2. Uses a classification algorithm to calculate the “borrower risk index”
3. Calculates the risk index.
1 – low 100 – high
www.pi-cube.comBorrower Risk Gap• Creates a risk interval that highlight events that could trigger
losses.
1. Risk Index 2. Risk of an adverse event(Represents combined risk of Default or Late payment(s))
3. Risk of Late Payment
4. Underwriter Discretion(By examining the interval, an underwriter is able to refine the applicant’s risk profile.)
Past DueFina
l Not
ice
www.pi-cube.comBorrower Risk Gap
0 100
28 33 45
2. The Risk of adverse event is 33%.
3. The Risk of late payment is 45%.
With a Risk Gap of 28% to 45%, this applicant is a relatively safe borrower.
2. Risk Index is 26%.
www.pi-cube.comBorrower Risk Gap
3. Risk of late payment is 60
1. Risk IndexBy using only this measure, one would normally assume that this applicant is an ideal candidate. However, #2 and #3 gives us more clarity
2. Risk of adverse event is 55
With a Risk Gap of 42-60%, it is difficult to determine whether this applicant is safe or risky. However, with a 55% risk of late payments, it seems that, though the applicant is unlikely to default, he/she may be prone to making late payments.
1000 42
55
60
www.pi-cube.comHow we build it.
Subject-matter
expert to pinpoint a suitable plan of action
Data preparatio
n and industry research
Validate and test
the output model to ensure a
high accuracy
1 2
3
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5
Exploratory Data AnalysisExplore attribute distributions, correlations, factor interactions, etc. Determine whether findings are appropriate. Fix if necessary.
Subject-Matter ExpertEngage SME for more information on the consumer lending industry and qualitative observations on particular behavior regarding the client’s portfolioData Prep/Industry ResearchPrepare dataset for analysis. Perform research on modeling techniques that others have used to quantify consumer behavior in the space. Modeling ExerciseFit different classification models. Explore options such as Feature Selection to numerically determine the most important factors. Validate & TestCompare different models using cross validation. Evaluate based on prediction accuracy and interpretability. Choose the best performing model.
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The Process
85 Attributes to Analyze
3
4
5
Perform exploratory analysis to
identify data structure & modeling
techniques
Initiate modeling exercise
accounting for ad-hoc
adjustments
www.pi-cube.comHow we build it.
Client Financial Profile Attributes1) Annual Income2) Debt-To-Income Ratio3) Revolving Credit Utilization4) Length of Credit History5) Employment History6) Homeownership Status
Macroeconomic Factors1) Unemployment Rate2) Inflation Rate3) Real GDP Growth4) Consumer Expectation
Modeling Techniques used1) Logistic Regression2) Feature Selection3) Data Aggregation & Discretization4) Cross Validation5) Resampling Methods6) Performance/Prediction Evaluation
• From 85 attributes, reduction to the 10 most impactful attributes
The Results
www.pi-cube.comExampleAmy Smith has just been appointed as head of the struggling, consumer loans division of a large bank.
She inherits a business that has been in steady decline for three consecutive years.
She’s looking to adjust lending policy to trigger a turn-around.
Her goals:
1. Stop the losses
2. Guide the business back to profitability
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Scenario A: Stop the losses
Amy uses Predictive Analytics to:
•Explore and analyze the current portfolio of borrowers based on their Risk Index •Extract key insights about the overall risk of the business •Fine-tune the lending criteria
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Scenario A: Stop the losses
She is now able to:
Develop more targeted policies based on
overall portfolio risk
Uncover relationships between her clients’ financial and the state of the
economy.
Make a more robust analysis of the current state of the business, leading to better lending decision
in the future.
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Scenario A: Stop the losses
• In , Amy sees the spread of the portfolio risk index. The bar graph suggests that the bank offers many low risk loans.
1
DefaultNon-Default
1
Num
ber
of
Borr
ow
ers
Risk Index
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Scenario A: Stop the losses
• In , we see the spread of annual income across 3 levels of homeownership status (own, rent or mortgage).
2
DefaultNon-Default
2
Annu
al In
com
e
Own MortgageHomeownership Status
Rent
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Scenario A: Stop the losses
• In , we see the relationship between debt-to-income ratio and the loan amounts offered.
3
Loan A
mount
Debt-to-Income Ratio
3
www.pi-cube.comScenario A: Stop the losses
• Figure , Amy sees a large number of high risk clients in the portfolio (in red).
1 Amy sees in Figure and , that the concentration of these high risk clients either own or pay a mortgage and have a debt-to-income ratio that exceeds 15.
In Figure , she observes that for individuals with high debt-to-income ratio, the loan amount is not a significant factor as defaults can be found across different loan amounts for individuals with high debt-to-income ratio.
OBSERVATIONS
2 3 3
www.pi-cube.comScenario A: Stop the losses OBSERVATIONS
Based on the analysis of the portfolio, Amy finds that individuals who own a home and have less than $60,000 annual income make very risky borrowers.
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Scenario B: Growing the business
Amy faces the next challenge.
She is looking to develop a strategic development plan to grow the customer base and expand operations.
She would like to know when and where to begin, as well as which market segment to focus on.
The bank currently operates in all 50 states.
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Scenario B: Growing the business
Amy uses Predictive Analytics to analyze her portfolio of customers to observe trends and get insights about potential portfolio behavior in the future.
The bank maintains a detailed financial profile for each borrower including: • Annual income, • Debt-to-income ratio, • Employment history, • Home ownership status, • and Credit utilization.
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Scenario B: Growing the business
She is now able to:
Observe the results in real-time on a mobile device
Using easy-to-understand visuals that provide key business insights
Make game-changing decisions for her business
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Scenario B: Growing the business
Amy looks at the spread of client debt-to-income ratio in the portfolio across the nation, based on region. She takes note of the distribution of defaults across the regions and across varying levels of debt-to-income ratio, and compares it to the volume of loans in each region.
1
Region
Debt-
to-I
nco
me
Rati
o
West Northeast
South Midwest
OBSERVATIONS
KEY INSIGHTAmy finds that the Midwest region has the highest percentage of defaults.
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Northeast
Scenario B: Growing the business
Amy sees the spread of the (predicted) risk index across the nation. She notes the distribution of defaults across the regions vis-à-vis levels of risk. She also notes the concentration of defaults in the high-risk section.
2
OBSERVATIONS
KEY INSIGHTAmy sees that the South represents the highest risk as a region. There are more clients with a high risk index, as compared to the West and Northeast.
Region
West
South
Midwest
Risk Index
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Scenario B: Growing the business
Using her Predictive Analytics application, Amy finds that that an unemployment rate of 6.5% is a key number.
3
DefaultNon-Default
KEY INSIGHTAmy infers that unemployment rate greater than 6.5% corresponds to a greater number of loan defaults.
OBSERVATIONS
Loan Amount
Unem
plo
ym
ent
Rate
www.pi-cube.comScenario B: Growing the business
• WHERE: From Fig. 1 and 2, Amy has inferred that the West region is the safest candidate for conservative growth as there are more individuals with lower debt-to-income ratio corresponding to a lower regional risk index.
• WHEN: Based on Fig. 3, Amy sees possibility of business growth during a period when the unemployment rate is less than 6.5%.
• WHO: Fig. 1 tells Amy that targeting individuals with low debt-to-income ratios in the West would be the safest bet.
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AMY’S DECISION
• Target the Western coast • Target individuals with low debt-to-income ratios • During a period when the unemployment rate hovers
around 6.5%
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