Machine Learning is shaping up to be one of the top 10 key technology trends of 2017. But how can banks use this technology to meet their business objectives and improve performance today?

What is Machine Learning?

Machine Learning is an application of Artificial Intelligence that allows computers to learn without being explicitly programmed to do so. It’s the product of established statistical theory and more recent developments in computing power. Combined, the Machine Learning algorithms offer businesses the opportunity to transform their operations and the services they provide.

These algorithms are categorised as being supervised or unsupervised. They learn by analysing very large volumes of quantitative and qualitative historical data, looking for patterns and trends across hundreds of variables at the highest speeds – something a human could never achieve alone. In a supervised learning case, knowledge gained is applied to new data in order to make predictions and therefore better plan for the future. In the unsupervised case, it’s used to describe the data’s “hidden structure,” such as for customer segmentation or anomaly detection purposes.

Where and how can it be applied?

Given developments in Natural Language Processing (NLP) and voice recognition, Machine Learning can be used in back office operations as well as communicating with customers and clients. Below are some of the key areas banks could benefit from by applying Machine Learning:

  1.        Fraud Detection

Traditional fraud systems identify fraudulent transactions based on specified, non-personalised rules, such as if a customer spends money abroad. Machine Learning systems, on the other hand, analyse large amounts of each customer’s transactions to understand their personal spending patterns. This way, they can spot subtle anomalies that indicate potential fraud. Each transaction is automatically analysed in real-time, and is given a fraud score which represents the probability that it is fraudulent. If it is above a certain threshold, a rejection is triggered immediately. This would be extremely difficult without Machine Learning techniques– as a human could not review thousands of data points in milliseconds and make a decision.

  1.        Credit Risk Management

The current credit risk workflow tends to be labour intensive, slow and riddled with judgement related human-errors. Machine Learning credit default prediction models allow for more accurate, instant credit decisions as they can automatically use a much broader range of data sources including news and business networks. The algorithms can also be used to improve Early Warning Systems (EWS) and to provide mitigation recommendations, based on previous responses. The result is lower rates of default losses whilst also reducing the risk of losing customers to competitors due to a slow process.

  1.        Risk and Finance Reporting

Cognitive automation appears to be the next development in the world of automation, post Robotic Process Automation (RPA). RPA allows a business to map out simple, rule-based processes and have a computer carry them out on their behalf. Cognitive automation, however, combines this with the ‘thinking’ work of Machine Learning, and programs computers to read and understand unstructured data or text and make subjective decisions in response, similar to a human. This has the potential to transform back office risk and finance reporting processes, enabling banks to meet regulatory reporting requirements at speed, whilst reducing costs.

  1.        Trading Floors

Machine Learning can be used in a variety of ways on trading floors. From a compliance perspective, it can undertake behavioural analysis by reviewing trade activity for each employee alongside mining chat-logs and emails to identify suspicious activity. From a performance perspective, Machine Learning algorithms autonomously evolve and search for new patterns in internal and external, quantitative and qualitative data, making real-time high-frequency trading decisions to exploit volatility in individual stocks. This has the potential to increase trading performance whilst also reducing compliance risks.

Conclusion

With an unstable economic environment creating new risks, with profit margins trending down and continued high regulatory pressure, banks should be exploring the power of Machine Learning across their operations.  The benefits include lower cost bases and improvements in the effectiveness and efficiency of processes, whilst also providing a better level of service and enhanced products and services to their customers.

For more information, please view the presentation:Machine Learning In Banking

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