Suddenly fruitful after years of sparse adoption, the long-awaited flowering of artificial intelligence (AI) and machine learning is upon us.
Copyright by m.bankingexchange.com
Risk management and compliance leaders can expect these advanced analytic technologies will propel productivity-enhancing applications for years to come. But how did we get to this point? And as we enter the third decade of the 21 st century, what can we anticipate right around the corner? Planting seeds
AI owes its recent gains largely to the accumulation of big data assets and the continually declining cost of computing. Together they are the catalyzing spark behind the growth of AI, machine learning, computer vision, natural language processing and other analytics varieties.
Risk management and compliance applications saw a major transition between 2017 and 2019 in the role AI/ML played in risk management. Two years ago, not all organizations were considering AI, and those that did were primarily looking for use cases. These early AI adopters appeared to be driven more by a desire to participate in the technology than to fill an organic need with it.
Today’s landscape is significantly different. Nearly all enterprise-sized financial institutions are engaged in AI-associated machine learning (ML) projects and leveraging them for real business – improving customer experience, fraud detection and, of course, risk and compliance functions. In the risk and compliance arena, we can expect new applications of AI in credit decisioning, model risk management and governance, and stress testing. ML will also drive natural language processing and intelligent automation to assist many GRC tasks. An AI bouquet
While some believe that AI will eliminate the roles of certain workers in the coming years, that outcome is highly unlikely in risk and compliance. Instead of displacing employees, AI and ML will make them more productive and efficient. Advanced analytics will take over many of workers’ more routine tasks, freeing highly skilled staff to turn their talents to more productive duties. There will also be significant and quantifiable improvements in decision-making as the industry advances in some key arenas.
Productionizing machine learning in credit decisioning begins. There have historically been two key challenges in the adoption of machine learning in credit decisioning: first that ML models are hard to explain, and second that ML models sometimes don’t achieve the accuracy improvements that make them worth pursuing.
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Explainability is being addressed by a wide community of ML experts, as it is essential for ML adoption across a number of industries. Cross-industry efforts have led to a variety of techniques for explaining ML models, as well as generating constrained interpretable ML models. Methods for generating credit scorecards from a variety of ML models are underway, including a model-agnostic approach that will support scorecard generation from any model.
The challenge of meeting accuracy expectations is being tackled through the gathering and incorporation of more data into credit decision models. The volume and nature of data traditionally fed into credit scoring models were suited to and tailored for linear models. Non-linear models like neural networks and gradient boosting had little to find in the data beyond what the linear models found.
However, as new data is available (banking transaction data, for example) and incorporated into decisions, the accuracy of more advanced machine learning will be significantly better than traditional linear models. Banks’ desire to extend credit into markets previously blocked due to lack of credit history will drive the incorporation of this new data and adoption of ML models. […]