probability of default (pd) model regression

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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


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


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



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



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



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


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


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


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


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


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


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


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

probability of default (pd) model regression

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