INTELLIGENT RISK MACHINES in NIGERIA FINANCIAL INSTITUTIONS
In this regard, RPA, AI and machine learning are areas of huge potential. At this stage, few risk teams have fully exploited these technologies, but most have now at least begun to explore them. For instance, just 8 percent of banking respondents to the 2017 Global Risk Management Study claim to have highly proficient capabilities in RPA, while 25 percent are using it in the risk function but are not extracting its full potential. AI has similar figures: 10 percent and 31 percent respectively (see Figure 2).
Banking respondents in our study selected “improved customer service” as the greatest opportunity for the risk function arising from adopting RPA. This itself is reflective of the increasingly integrated role of risk management within banks, and how risk automation is seen as a fundamental driver of the speed, efficiency and productivity gains that support many new, digital services customers now expect. RPA also presents banks with a wide range of opportunities: many 2017 study respondents (close to a third in each case) also believe the technology can help them gain “improved risk analysis and risk insight,” “greater efficiency/ productivity” and “improved compliance.”
In addition, robotics (and other forms of automation) and AI (including machine learning and other disciplines) overlap depending on the application. And the list of applications is expanding rapidly: reviewing disclosures, scanning transactions, assessing incident reports, picking up anomalies in employee activity, predicting drivers of change to risk exposure, and rapidly checking data quality. There are more possibilities to explore than the time and resources with which to do so.
Thinking about the range of technologies that you use to support your risk management function, how advanced is your institution’s use of the following technologies? (Base: 159 – banking)