Singapore-headquartered OCBC Bank has piloted two financial technology solutions from companies Scila (Sweden) and Cardabel (France) using artificial intelligence for internal controls. The AI solutions augment the bank’s competency in the detection of trading anomalies during the audit of trading activities. This is done by analyzing trade data using machine learning algorithms.
The monitoring of trading activities to detect potential trade anomalies is currently carried out on multiple fronts by the Bank. These include daily checks by risk and control units and, as a last line of defense, by trained internal auditors. Today, it can take an auditor up to three months to complete an audit of trading activities. The process involves considerable time to extract and sort voluminous trade and market data before manually analyzing them to detect potential irregularities based on a set of pre-determined indicators. Thereafter, the auditor will investigate the irregularities.
It is ideal to deploy AI solutions for the auditing process as trading activities generate large volumes of digitized data. The following benefits are expected: greater ability to integrate multiple and voluminous data sets for more timely and complex analytics in a shorter time; and better insights such as identifying ‘unknown-unknowns’, which are anomalies that were previously unknown to the auditor, can be unearthed.
Swedish fintech Scila has developed more than 100 market abuse indicators – a preset list of known anomalies such as insider trading. Exceptions flagged and investigated can be fed into their supervised machine learning system to refine the market abuse parameters so as to improve the detection of “true positives”.
France’s Cardabel meanwhile uses unsupervised machine learning to detect both known and unknown types of trade anomalies. Its algorithms do not require preset rules to look for unusual trade patterns that have not been identified previously.
In a statement, Goh Chin Yee, OCBC Bank’s head of Group Audit, said that “AI has shown an ability to not just analyze huge volumes of data and generate meaningful insights but be a powerful tool in identifying the unknown-unknowns in trade anomalies. Through AI, we will be able to further augment our audit effectiveness.”