The lucrative business of banks selling their customers “financial advice” coupled with financial products has been in the spotlight recently with the events that have shaken up the banking and financial services world. The most notable ones are the investigations of the Royal Commissions into the misconduct of the banking and superannuation industry1, the misconduct charges of £264 billion fined by EU on the top 20 banks2, the Wells Fargo fake accounts scandal3 and the nearly 1000 cases of fraud reported to FINRA resulting in a fine of $61 Million in 20184.
In an industry that relies heavily on trust surely everybody could benefit from banks taking the high ground as customers continue to bank on the trustworthiness of the advice they receive, thus, creating a positive feedback loop.
Interestingly, as boards of banks and their senior leadership take action on getting a fair and objective assessment of their risk appetite and solutions available to deal with fraud and compliance, the CIO organization needs to step up by recommending some of the strategic capabilities offered by AI and ML solutions as these will help immensely.
The following five brass tacks are key in exploring of how AI and ML can help in fraud detection and achieving compliance.
Brass Tack 1: A shift to pattern-based detection for improved control
The traditional approach to detect anomalies and frauds relies heavily on rules and as such, follow simple instructions laid by humans. This approach has its own limitations. Rules are rigid, straightforward and produce a large amount of false positives that banks need to investigate by spending a lot of money. While doing so, banks have realized that fraudsters find their way in circumventing these rules by evolving newer ways of perpetrating crimes. Therefore, banks that are tied to rule-based systems find it hard, time-consuming and costly to adapt to evolving patterns of fraud.
They need to look at augmenting rule-based models with mechanisms to self-learn over time. Understanding patterns in data is something that cannot be achieved by traditional systems and that is the key here as new patterns of fraud emerge.
Brass Tack 2: Appreciation of the importance of large volumes of datasets
Ask any data scientist what their “holy grail” is. Bang comes the response – large volumes of “labelled” datasets.
Getting large volume of relevant datasets that have been labelled with the outcomes of events that occurred earlier is easier said than done. Why? This is because:
- The labels on the data might not be readily available. Do banks capture information on which customer have complained about product mis-selling and integrate this with the CRM system? If the two are separate datasets, then can you tie a customer in one system with the same one in the other with a unique key or identifier?
- Not all data is born equal. Data needs to be profiled and outliers need to be detected and treated before the same is deemed fit for consumption by AI & ML models. Techniques such as Box-plot, Cook’s distance and Z-score come handy in detecting outliers.
- Dealing with issues of data security has never been more difficult. Sharing data with outside firms is all the more difficult. Anonymization, pseudonymizing, encryption, localization of data is needed for compliance with data security regulations such as GDPR. Banks are grappling with an appropriate strategy for the same.