Credit Risk Modelling

Consumers have now come to expect modern solutions from banks that can use their access to consumer data to create excellent credit card recommendations and other financial products. Banks can also use this information to make quicker decisions and reduce fraud. They can use predictive analytics to drive operations.


Banks have been relying on outdated methods of determining credit-worthiness. Banks typically use existing credit history, length of credit usage, and payment history, but these methods are often not enough. Risk managers and lenders also need to identify, measure, and mitigate the risks involved around lending. They do this by deriving expansive, in-depth risk models and credit profiles for different and borrowers, and predict defaulting probabilities and prevent such occurrences.

Defaulters increased by 8.3% and 13.25% from 2014 to 2019 in the retail and corporate segments, exposing severe limitations in present credit risk modeling systems.

Solution Approach platform provides out-of-the-box development frameworks.  The project was started with the relevant environments, which were then created automatically. Development images configured based on pre-defined templates were installed on-premises or in a development VM within the infrastructure. This enabled authentication using LDAP, seamless project setup using Bitbucket, Jenkins, and Docker (ensuring build and deployment without software compatibility issues).

The platform made available by leverages the latest ML and DL tools while preparing models. It includes Pachyderm-based data versioning, Kubernetes, Kubeflow, and Spark-based ML and DL. It also includes an Istio-based service mesh-enabled microservice architecture, and ELK-based monitoring capability, contributing to a reduction in latency time.’s MLOps platform allows establishing high-end Alluxio and Presto-based efficient data connectivity and collecting data from diverse sources. A major part of the data transformation journey while creating models involved setting up the required infrastructure and collecting raw, continuous, unformatted, unparsed data from many social media channels, viz. LinkedIn, WhatsApp, Facebook.  Details such as name, current employer details, designation, years of experience, employment history, 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, analyzed and added as exploratory variables.

By using data versioning and connectivity libraries, data versions were easily controlled and stored into xpresso Data Model (XDM)-enabled data store. This enabled easy retrieval and storage of datasets/ files into internal XDM. The attributes obtained were used for categorization (employing Pachyderm-based data versioning) and then performing univariate, bi-variate, and Bag of Words analysis — for both structured and unstructured datasets through xpresso Exploratory Data Analysis (Data and Statistical Analysis).  Different datasets and their different versions were easily controlled and stored into an xpresso Data Model (XDM)-enabled data store that enabled easy retrieval and storage of datasets/ files into internal XDM. This was achieved using two excellent features of

  1. Data Connectivity Marketplace libraries
  2. Data Versioning

The xpresso Data Pipeline Management (Rapid Model Training and Experimentation) uses Kubeflow-enabled pipelines. Thus, multiple experiments using different models and datasets could be created, tested, paused, and restarted to gain better insight. reads factors from various text connections and generates an output. From all these variables obtained, models were created, versioned, trained, and evaluated with both standard and custom metrics, finally rendering a Credit Scoring model. 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.

How can help Finance Organizations transform their journey to cognitive AI solutions is an AI/ML Application Lifecycle Management Platform. enables complete lifecycle management of AI/ML solutions, addressing the AI transformation journey of enterprises on any cloud platform of choice. offers functionality essential for building AI/ML solutions – primarily enabling data scientists to rapidly build predictive and prescriptive models. The platform provides a user-friendly interface to develop, deploy, and manage AI/ML solutions at scale. In addition, supports the incorporation of these solutions into business processes, surrounding infrastructure, products and applications.

Key benefits of include:

  • Empowers data scientists to transform AI/ML research into solutions 
  • Improves the productivity of data scientists by enabling them to focus on the business problem, developing algorithms and rapid experimentation of models 
  • Addresses the shortage of skilled data science resources with automated workflows, toolkits and frameworks 
  • Manages AI transformation journey costs without any wastage of R&D efforts  
  • Provides an enterprise-ready and secure environment for complete lifecycle management of AI/ML applications
  • Enables at-scale deployment of enterprise AI/ML applications on-premise, cloud (AWS, GCP, Azure), or hybrid environments

Additional details on can be found at: We can schedule a demo of the platform for anyone interested in learning more.

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