Home>Credit Risk Modelling Use Case v.1.3


Consumers nowadays expect bespoke, platform-agnostic solutions from banks and financial institutions, who in turn can leverage their deep access to varying and huge volumes of existing customer data – such as employment history data, social media data, shopping and purchasing patterns to create best-fit solutions for credit opportunities Banks and financial institutions can also review, analyze and interpret this data to prevent financial frauds and risks, engage in more efficient and quicker decisions, and envisage on-point, targeted, predictive analytics that drive services, operations and products.


Determining credit has been done by traditional techniques for decades that doesn’t factor in various aspects or features of a corporate or retail borrower’s credit ability. Minimal data, usually, existing credit history, length of credit usage, and payment history have been used for credit risk scoring. Risk managers and lenders also need to identify, measure, and mitigate the risks involved around lending by deriving expansive, in-depth risk models and credit profiles for different and borrowers, predict defaulting possibilities and prevent such occurrences.

Defaulters increased by 8.3% and 13.25% during 2014 — 2019 in the retail and corporate segments, respectively, exposing severe limitations in present credit risk modelling systems.


xpresso.ai is a Auto-ML AI Ops platform that excels in data collection to inference generation. The Auto-ML framework made available by xpresso.ai leverages the latest ML and Deep Learning tools while preparing models.

Data Collection and Analysis

A major part of the data transformation journey while creating models involved setting up the required infrastructure and collecting raw, continuous data (unformatted, unparsed) from a huge number of social media channels, viz. LinkedIn, WhatsApp, Facebook.

xpresso.ai’s Auto-ML AI Ops framework was leveraged to collect and analyse this extensive data repository and analyse details. Details such as name, current employer details, designation, years of experience, employment history, as job rotation, years of experience, frequency of job change, key skill endorsements, group memberships, friends, online behavior, buying patterns, from both Facebook and LinkedIn were collected, analysed and added as exploratory variables.

Data Cleansing

xpresso.ai extends data libraries that aid connecting to these data sources, collecting data off those as well as credit ranking and banks, and also helped in data correction such as cleansing, standardization, removal of stale and extraneous data.

Data Preparation

xpresso.ai can read factors from a varied recommendation text connection and generated an output. This was supplanted with additional data collected (with the aid of xpresso.ai libraries) while preparing a predictive model.

Model Processing/Evaluation

From all these variables obtained, models were created and versioned — enabled by xpresso.ai Auto ML-AI Ops framework.These models were also trained and evaluated with both standard and custom metrics, finally rendering a Credit Scoring model.

Inference Generation

xpresso.ai was able to predict customer credibility with near 95% accuracy.The accuracy of predictions decreased the default rate by 3.23% and prevented a potential loss of $1.2 million.

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