Risk management in banking may be seen as a logical development and execution of a plan to deal with potential losses. Usually, the focus of risk management is to manage a financial institution’s exposure to losses or risk and to protect the value of its assets. Theoretically, different activity classes are part of banking, but the general classification is based on traditional banking and trading activities. Overall, banking activities create many unique risks that are 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 regulations that emerged from the global financial crisis in 2008, the fines levied in its wake and stepped up to the challenge of reigning in model risk across the financial industry.
Banks are now leaning towards business modeling that requires that risk managers to understand and manage model risk better. Risk management functions can now leverage faster, cheaper computing power to process a variety of customer information. This has potential to assist banks to take better credit risk decisions, assess portfolios for early evidence of problems, detect financial crime, and predict operational losses.
As banking regulations continue to broaden and deepen, largely driven by public sentiment that is 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 improve them.
Also, as the need to adapt to market developments require rapid, fact-based decision making, better risk reporting is required. While regulatory requirements have optimised 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 still 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 leaner and faster.
Continuously emerging innovations such as big data and machine learning illustrate the impact of new risk-management techniques that help make better risk 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 or 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 can be expected to use these models for a number of purposes, including financial-crime detection, credit underwriting, and develop early-warning 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 machine learning and Deep Learning tools while preparing risk models. Traditional model development methods are lengthy, tedious and often prone to human bias.
xpresso.ai’s Auto-ML AI Ops framework allows automating variable selection, data partitioning, model performance testing, model tuning and so on instead of manual coding.
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 and banking databases. However, although a model may be built as intended, it might produce inaccurate outputs when compared to its design objective and intended use or a model may be used incorrectly or inappropriately, or its limitations or assumptions may not be fully understood. To address these possible risks, the model development and implementation were aligned with industry best practices and strict quality control. xpresso.ai’s Auto-ML AI Ops framework was leveraged to collect and analyse this extensive data repository and analyse details.
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.
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.
Before these models were deployed to production, they were independently reviewed. This ensured that their behavior was aligned with their design objectives and business uses. 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 to provide an effective challenge to each models’ development, implementation, and use. xpresso.ai’s Auto ML-AI Ops framework also allowed quick reproduction of the model development process, thus enabling model validators monitor and review the model and its potential limitations more closely.
Based on the risk models generated by xpresso.ai, 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.
