5 MINS READ
The biggest questions need the boldest answers. That's why we're using our global scale, technology expertise, and collaborative spirit to move towards a better today and a brighter tomorrow together.
Strategic discussions amongst the C-suite and regulators include forward-looking themes such as how AI-driven technology and analytics can strengthen risk management. Or how AI can help an organization transition from a cost center to delivering business value. They might also cover how customer experience can be enhanced, i.e., by transforming risk processes in a manner that raises the customer satisfaction metric Net Promoter Scores (NPS).
First and foremost, the identification of top risk priorities is critical. These will vary from one financial institution to another based on the sector, required regulations, customer segments, and an organization’s business priorities and objectives. Ultimately, a bank’s strategy will drive its appetite for risk. Once this is determined, appropriate tools and techniques can be identified after reviewing available data and building algorithms to gauge the proper set of models. For organizations to understand how quickly these interventions can be implemented, they will need a thorough knowledge of how data gets generated, stored, and used.
Diagnostic tools can then alert users on unexpected events or metrics using anomaly detection algorithms. Dashboards incorporating machine learning model outputs can help predict risk outcomes and NLP can be used to generate narratives for better understanding risk profiles and underlying exposure changes.
Financial institutions find it challenging to deal with existing risks or to even effectively identify them in a timely manner since they manifest themselves in unfamiliar ways.
It is crucial to start with a clear definition of a problem statement, specific end-state objectives, and time-bound milestones of the implementation journey. A completely new process is not necessarily essential. Instead, fine-tuning existing methods can help realize the benefit of new technology interventions. It is highly recommended that organizations define and track KPIs linked to business imperatives to better measure outcomes, such as increasing the size of control testing samples, making better and faster credit decisions, and reducing false positives.
Adopting an AI-driven approach also requires a change in the mindset of the teams involved, a business’ comfort with experimentation and acceptance of the occasional failures, an adoption of the learnings revealed from such trials, and a willingness to change the nature of roles and needed skills of the personnel involved. With this in mind, we see increasing numbers of data scientists recruited by risk units across financial institutions in Australia, changing the overall focus of the workforce. Some organizations have up to 60% of the workload of a risk team focused on regulatory reporting and risk operations. Using new technologies and introducing new skill sets can give more time for data analysis, which creates new value streams and, more broadly, a wide-reaching shift change for an organization.
Finally, the importance of strong governance over the deployment of AI cannot be overstated. The knowledge that these technologies depend on a continuously evolving dataset puts the onus on firms to validate and calibrate the underlying algorithms. An increased risk of inappropriate feedback going undetected in those AI solutions that allow for continuous feedback and learning may compromise a solution’s ability to produce accurate results.
Improved capital management to optimize profits: Building, testing, and managing better risk and capital models can allow for the deployment of funds more efficiently. AI can help manage model performance, calibration, validation, and analytics.
Automation of credit decisions: The ability to compute data from multiple sources, including social media, leads to better pricing for low-risk customers and better management of collections based on data patterns.
Climate risk assessment: The Australian Prudential Regulation Authority (APRA) recently initiated an exercise to design and deliver a climate vulnerability assessment (CVA) in collaboration with prominent Australian authorized deposit-taking institutions (ADIs). The CVA will quantify aspects of banks’ exposure to climate risk and provide insights into the analytical and capacity challenges facing entities engaging in climate risk scenario analysis.
Digital farming solutions: AI models use remote sensing data to derive expected crop yields based on climate monitoring, grading, and acreage.
AI will become an integral part of the risk strategy for many financial institutions to deliver better customer service, provide oversight over business operations, and gain a competitive advantage.
Since AI in financial services is still in its early stages, there will be a learning curve wherein stakeholders such as risk management teams and business and control functions will need to increase their technical understanding. Organizations that identify such cross-functional teams and incentivize them to collaborate will better exploit the benefits of AI.
The emergence of Regtech players who can curate niche solutions catering to specific industry needs is another channel for financial institutions to realize these objectives.
When adopting AI, firms will need to evaluate how to encapsulate specific interventions into their risk management framework.
They must ensure that the solutions remain fit-for-purpose and provide confidence that the AI solution can function within the boundaries set by a firm’s culture and risk appetite.