Risk Analysis

Risk management in banking means figuring out ways to deal with potential losses. Risk management usually focuses on managing a financial institution’s exposure to losses or risk. It also tries to protect the value of its assets. Banking can be broken down into many different types. However, this focuses on traditional banking and trading activities. Overall, banking activities create many unique risks related to a bank’s credit, liquidity, trading, revenues and costs, earnings, and solvency issues.

Traditional risk management in banking was driven by industry best practices rather than regulatory standards.  However, financial regulators around the world responded to the 2008 Financial Crisis with new regulations. On top of fines, they started reigning in model risk across the financial industry.

Banks are now leaning towards managing model risk better. Risk management functions can now leverage faster and cheaper computing power to process a variety of customer information. It has the potential to assist banks in making better credit risk decisions, assess portfolios for early evidence of problems, detect financial crime, and predict operational losses.


As banking regulations continue to grow, mainly by public sentiment growing increasingly intolerant of bank failures, banks have to up their game while handling money. For instance, a large Asia–Pacific bank lost $4 billion when it applied interest-rate models that contained incorrect assumptions and data-entry errors. This illustrates the need for risk mitigation that embraces strict guidelines, develops and validates accurate models, and continuously improves them.

Adapting to market developments requires rapid, fact-based decision-making and better risk reporting. While regulatory requirements have optimized and improved the data quality used in risk reports, the format of reports or how they could be put to better use for making decisions remains largely untapped. Replacing paper-based reports with interactive solutions that facilitate real-time information and enable users to do root-cause analyses would enable risk management to be leaner and faster.

Emerging innovations such as big data and machine learning illustrate the impact of new risk-management techniques that help make better decisions at lower costs.  Machine learning improves the accuracy of risk models by identifying complex, nonlinear patterns in large data sets containing structured, semi-structured and unstructured data. Businesses can use these to detect, mitigate, and study emerging risks. They can also use them as the potential for such risks from both existing and potential customers’ portfolio and social media presence analysis. Coupled with the bank’s appetite for assuming risks and the flexibility to adapt its operating models to fulfill any new risk activities, banks can deliver greater value to customers while safeguarding capital. Risk functions use these models for a number of purposes, including financial-crime detection, credit underwriting, and develop early-warning systems.

Solution Approach

Traditional model development methods are lengthy, tedious, and often prone to human bias.’s platform allows automating feature selection, data pipeline management, model performance accuracy measurement, and model tuning leading to continuous software delivery, decreased complexity, and faster problem resolution. 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 framework 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 such as social media channels, credit ranking data, and bank databases. A major part of the data transformation journey while creating models involved setting up the required infrastructure and collecting raw, continuous, unformatted, unparsed data.

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

However, although a model may be built as intended, it might produce inaccurate results. This is even when compared to its design objective and intended use. A model may also be used incorrectly or inappropriately, or its limitations or assumptions may not be fully understood. Model development and implementation were aligned with industry best practices and strict quality control to address possible risks.

Before these models were deployed in production, they were independently reviewed. This ensured that their behavior was aligned with design objectives and business uses. From all the variables obtained, models were created and versioned — enabled by MLOps platform. These models were also trained and evaluated with both standard and custom metrics to provide an effective challenge to each model’s development, implementation and use.’s MLOps platform also allowed quick reproduction of the model development process, thus enabling model validators to monitor and review the model and its potential limitations more closely. read factors from a recommendation text connection and generated a result. Based on the risk models generated by, banks were able to screen high-risk customers with high accuracy, identify the degree of risk associated with potential charge-offs and possible delinquency and remove them on time, and also screen deals that involved such risks.

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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