Probability of Default (PD) model regression
Transcript of Probability of Default (PD) model regression
1 © 2020 Copyright Genpact. All Rights Reserved.
CASE STUDY
Probability of Default (PD) model for a leading bank using hazard logistic regression
Impact▪ Addressed regulations across accuracy and sensitivity
▪ Flexibility to use the model for various use cases, from stress testing to allowance and life-time loss estimation
▪ Ability to use the same framework across other retail portfolios to drive standardization in model building
Solution▪ Built an account level hazard logistic regression model using
internal loan performance data, origination variables, and credit bureau attributes that considers macroeconomic drivers
▪ A standardized approach to segmentation, selection of explanatory variables, and stability and sensitivity testing
▪ Accuracy testing performed and demonstrated on both conditional and unconditional probabilities
Challenge▪ Lack of a Probability of Default (PD) model for the bank’s card
portfolio
▪ Lack of an account level model that can produce PD forecasts across multiple use cases
▪ Insufficient data to establish macroeconomic correlations to default, since the portfolio was started during the 2008 downturn period
▪ Existing regulatory observation for the bank lacking adequate sensitivity in the stress testing model
Banking and Financial Services ►Risk Analytics
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CASE STUDY
Data-driven analytics delivers $40 million cost savings for a Fortune 100 co-branded card issuer
Impact▪ $40 million in cost savings through data-driven analytics
and intelligent reporting
▪ 5%-7% reduction in operating year-on-year
▪ 25% increase in customer self-service
▪ 25% increase in “first call resolution”
Solution▪ Dedicated analytics center of excellence
▪ Defect-free measurement system complimenting technology investments
▪ Data architecture designed to eliminate operational silos
▪ Data integration to build a foundation for analytics frameworks
▪ Delivery best practices to generate sustained benefits, year-on-year
Challenge▪ Unoptimized contact center operations across 24 sites with
3500+ agents
▪ High operating costs and functional silos leading to a poor customer experience
▪ Lack of digital channels to provide a deeper understanding of customer preferences, experiences, and channel affinity
Banking and Financial Services ►Customer Experience
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CASE STUDY
$70 million in savings through redesigned customer journeys and processes for a leading financial services company
Impact▪ Identified $70 million in cost savings over a 5-year period
▪ Defined and prioritized 30+ KPIs out of 250+ metrics and developed automated scorecards for each customer journey
▪ Deployed 12 analytics frameworks and 20+ digital solutions as part of the new digital architecture
Solution▪ Reimagined the customer journey from front to back office
▪ Developed advanced digital architecture to support the transformation
▪ Developed an analytics framework to optimize omnichannel interactions for a superior customer experience
▪ Deployed an integrated data management framework to collate actionable insights and develop a single source of truth for customer interactions
Challenge▪ High dependency on non-digital channels and paper-based
statements
▪ Multiple data silos and inefficient processes
▪ No roadmap for digital transformation
▪ Poor self-service models and high volume of customer queries resulting in high operating costs and poor customer experiences
Banking and Financial Services ►Customer Experience
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CASE STUDY
Transformed customer experience using ‘journey mapping’ for a leading financial services company in the US
Impact▪ Journey Operations Centre (JOC) set-up for enhanced
governance across 20+ banking journeys
▪ 15% improvement in CSAT scores
▪ Over $2 million savings in operations costs through self service optimization, within 2 months of implementation
▪ 10% reduction in web-to-phone cross overs
Solution▪ Unified view of customer journey on CoraJourney360
▪ Multi-modal data extraction and enrichment: Aggregation of legacy enterprise data and high velocity multi-structured data (speech to text, digital footprints, journey touchpoints)
▪ In-built journey analytics layer including KPI design, interaction analysis and journey outcome measurement
▪ Automated journey maps aligned with advanced CX metrics like Customer Effort, Sentiments etc.
▪ Centralized journey operations center to drive accelerated process improvements across 20+ banking journeys
Challenge▪ Lack of visibility in customer behavior, preferences or pain
points across touchpoints and lifecycle stages
▪ Establish a scientific customer experience measurement system in an environment of disparate data systems
▪ Reduce latency in implementing customer engagement strategies due to the absence of end-to-end customer journeys
Banking and Financial Services ►Customer Experience
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CASE STUDY
Improved customer experience with advanced speech analytics, for a financial services company
Impact▪ 95% accuracy in complaint classification
▪ 25% reduction in customer complaints
▪ Significant improvement in NPS scores
▪ 5% reduction in the average handle time for complaints calls
Solution▪ Automated the complaints identification and
classification process using speech analytics
▪ Increased agent productivity using call handle time optimization solutions
Challenge▪ Struggling to manage a significant increase in customer
complaints impacting customer experience and loyalty
▪ Lack of embedded analytics to accurately identify and eliminate the causes of complaints
▪ Lack of required skills to implement speech analytics solutions
Banking and Financial Services ►Customer Experience
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CASE STUDY
Advanced speech analytics improve the collections process for an auto finance company
Impact▪ 100% call monitoring
▪ 30% improvement in agent compliance adherence
▪ $250k reduction in operating costs
▪ 10% improvement in customer promise-to-pay rates, resulting in a $3 million increase in collections
SolutionCreated a voice-to-text engine with the following features:
▪ Supervised machine learning to build business taxonomy, develop keyword lexicon, and turn speech expression into strategic intelligence
▪ Developed best practices for agent coaching and customer remediation
▪ Root cause analysis using text mining for clear and consistent call classifications
Challenge▪ Highly manual process, with only 1% monitoring of all
agent collections calls
▪ Subjective review process without classification of breaches into relevant categories
▪ Reactive remediation plan
Banking and Financial Services ►Collections Analytics
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CASE STUDY
Machine learning and computer vision protect profits at a leading consumer goods company
Impact▪ Increased potential for sales growth by up to 6% as a result
of optimized cooler operations
▪ Reduced stock-outs by up to 90% with real-time tracking
▪ Ability to predict inventory levels and optimize product mix, resulting in a potential 10% increase in profits
Solution▪ Installed vision sensors on the coolers to deliver real-time
alerts on the operating conditions of the coolers
▪ Enabled GPS on the sensors to track the location of coolers
▪ Used machine learning and computer vision to analyze sales growth, stock quality, and stock-outs
Challenge▪ Frequent cooler location changes across multiple retail outlets,
resulting in sub-optimal positioning of products in the cooler
▪ Inability to track cooler health and effectiveness, resulting in quality deterioration of perishables and lost sales
▪ Lack of data on sales trends – for example volumes and types of sales by day, week, month, year
Consumer Goods ►Computer Vision and Machine Learning
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CASE STUDY
Personalized, AI-enabled coupon system to help leading retail stores amplify brand sales
Impact▪ Improved sales strategy for the client and its retailers
▪ Increase in coupon redemption rates and new customer penetration
▪ Estimated 38% increase in sales linked to coupons
Solution▪ Developed a personalized coupon system able to analyze
customer data like purchase cycle, frequency, shopping trends etc.
▪ Created an algorithm to find coupon affinity scores for every customer
▪ Created an algorithm to identify the most appropriate coupon discounts tailored to each customer
Challenge▪ Developing an effective coupon campaign to amplify sales of
branded items at leading retailers
▪ Identifying and targeting the right customer with the right coupon every time
Consumer Goods ►Artificial Intelligence
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CASE STUDY
Faster, automated categorization of ecommerce products for a leading consumer goods company
Impact▪ Faster and accurate coding of eCommerce items
▪ Zero to low backlog of files to speed up go-to-market cycles
▪ Reduction of manual effort: work previously done by 7-10 full-time workers is expected to be completed by just 2-3 full-time workers
Solution▪ Created a web crawling engine and used robotic process
automation to extract item data directly from the website
▪ Built an algorithm to automate categorization and fetch brand data from the extracted information
▪ Developed a solution to effectively map and code each item
Challenge▪ Highly manual process to categorize details of products sold via
eCommerce: 16k items manually coded each week and each item takes 1 minute to code
▪ Difficulty in identifying and collating real-time data across various eCommerce sites
▪ Anytime the website structure changed, product coding needed to be updated too
▪ Unstandardized and complex product information
Consumer Goods ►Machine Learning
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CASE STUDY
Deeper shopper insights for the ecommerce portal of a leading hospitality company
Impact▪ A better understanding of consumer journeys from initial
engagement to conversion across multiple geographies and touch points
▪ Ability to analyze digital interactions and reveal the most common ‘path to purchase’
▪ Highly targeted promotions on Facebook for mobile users
▪ A 17% traffic increase through an optimized offer webpage
Solution▪ Extracted current website performance data from Adobe
Analytics
▪ Measured targeted promotion engagement using external tracking codes
▪ Measured campaign performance using Facebook Insights and DoubleClick
▪ Combined reports to track correlations between promotions and increase in traffic to offer page
Challenge▪ Inability to measure online sales performance through
company website High Tech and Manufacturing ►Marketing Analytics
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CASE STUDY
Smart event forecasting to improve engine failure prediction for an aircraft engine manufacturer
Impact▪ Improved forecasting to allocate engine resources more
efficiently
▪ Reduced field visits and increased productivity, creating $3 million of business impact
▪ Improved prediction accuracy by 10% compared to traditional failure risk models
Solution▪ Built a model to classify engines based on unstructured
inspection report data
▪ Developed a predictive failure risk model by analyzing historical data
▪ Performed image classification using deep learning to identify engine and part damage
▪ Generated an advance visualization dashboard to clearly showcase engine risk
Challenge▪ Inability to create an effective engine assessment tool
▪ Unable to identify damage within specific engines or parts
▪ Huge volume of structured and unstructured data, in a non-standardized format
▪ Rule based semi-automated systems unable to detect patterns
High Tech and Manufacturing ►Advanced visualization
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CASE STUDY
Reducing annual revenue risk for a wind turbine operator with a smart forecasting solution
Impact▪ Reduced annual lost revenue risk by $16 million, across
1200 turbines
▪ Reduced off-warranty costs by extending the remaining useful life of the fleet
▪ Reduced working capital and inventory costs by 35%
Solution▪ Developed a comprehensive reliability assessment
solution with KPIs, failure forecasts, performance analytics, automated sensor data processing, and engineering analysis
▪ Machine learning based prognostic model to determine component level reliability
▪ Text mining using structured data to differentiate between faults and failures and effectively assess the downtime impact
▪ Inventory optimization with failure forecasting to improve working capital
Challenge▪ Difficulty estimating the maintenance cost of wind turbines
over a 3-5 year period
▪ Lack of visibility in across turbine reliability and maintenance requirement at a fleet and individual level, resulting in huge financial risks
High Tech and Manufacturing ►Smart Forecasting
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CASE STUDY
Reducing aircraft engine downtime by 20%, resulting in cost savings of $50 million, for a global airline
Impact▪ Reduced engine downtime by 20% resulting in a $50
million cost saving over 3 years
▪ Proactive maintenance leading to improved visibility of engine failure risks
▪ Improved maintenance lead time
Solution▪ Created big data analytics algorithms to predict engine
failure and plan for asset maintenance and downtime:
- Studied a combination of airborne and ground operations data
- Consolidated data sources including engine signal data, service logs, replacement logs etc.
- Leveraged analytics algorithms and failure forecasting models
▪ Built a recommendation engine to suggest component replacement before failure to minimize repair times
Challenge▪ High aircraft downtime and unscheduled maintenance
▪ Lack of visibility into potential failure risks before the aircraft lands
High Tech and Manufacturing ►Machine Learning