4 Ways Predictive Analytics Improves Innovation

Lessons learned from the banking sector


Innovation is nothing new.

Unsurprisingly so, as today’s customer expectations are rapidly changing. Businesses that are unable to respond quickly to the shifts in consumer demands will eventually find themselves made irrelevant. In my previous article, I shared how culture is crucial to the innovation process - a company that encourages and provides the right environment to incubate innovative experiments are more likely to succeed. With the framework of creating a suitable ecosystem for innovation already laid out in the same article, the focus now will shift towards an accompanying tool that is critical in improving innovation - predictive analytics.

Predictive Analytics - A Bank’s Crystal Ball?

Advances in big data and the IoT has brought data collection to an unprecedented level, consequently refining data mining capabilities. The banking sector represents one of the many industries that can benefit directly from such improvements. Banks can now identify past trends that will provide insights on best practices and use the information to predict future trends that, in turn, present foresight into what is likely to work. Although its full potential has yet to be realized, can banks still utilise predictive analytics to enable precise forecasts of future business agendas and maximise the innovation potential today? The answer is yes, and here’s how:

1. Better engagement through personalization

When it comes to banking customers, personalisation is the secret ingredient to customer engagement. To a certain extent, it has evolved from being a novelty to a necessity. Consumers now expect their banks to understand their financial needs fully and translate such information into customized communications. Predictive analytics unlocks just that - it helps uncover the needs of each consumer in a far more granular way. Predictive analytics allows analysis beyond structured data, including unstructured information derived from customer activities online and social media.

Predictive analytics, coupled with AI technology, can also enhance the personalization capability of employees, as it can relieve them of repetitive and mechanical activities. With the use of suitable analytics software, more time can then be allocated towards human-centric activities such as customer engagement and innovative leadership. Such software is capable of providing rapid feedback and real-time analysis of customer feedback that cross-references nuances in language and tone, such as sarcasm.

2. Meeting consumer needs

The main objective for every bank is to meet each client’s needs. This is no easy task, as customer expectations are highly varied. Banks face dissatisfied clients due to an array of challenges, from poor matching of channels and products to negative interaction with the bank and so on.

With predictive analytics, such challenges can be alleviated, and even turn customers into advocates. This can be achieved by anticipating consumer behavior and presenting a solution even before the client expects it. Banks can be appropriately equipped to extract specific information and transform it into individual offers to please and retain their clients. After all, one of the best ways to keep your customers is to anticipate their individual needs and be their trusted advisor.

3. Optimise pricing and profitability

A more refined view of the profitability of different products could also be gained from using predictive analytics. An example of a dashboard of an analytics software is shown below-

Credit: Tandemseven

Acting on such data allows banks to make important business decisions, such as growing top-line revenue or even conceptualizing profitable customer relationships. For example, First Tennessee Bank found that initial data suggests that only 40 to 45% of products are profitable. However, further analytics uncovered that a handful of these unprofitable products can lead to a highly lucrative banking relationship in the future. Furthermore, predictive analytics is capable of providing insights into existing accounts of high net worth individuals to drive greater profitability, along with cultivating deeper client engagement.

4. Fraud detection and risk management

The benefits that predictive analytics provide for risk management in banks is considerable. On the client front, the more sophisticated nature of analytics can precisely determine the risk of credit default of each customer. On a product portfolio level, advanced analytics can calculate the overall risk based on multiple sources of data.

Analytics can also detect specific patterns that flag fraudulent activities, allowing banks to implement immediate counteractive measures. Specific data, such as the location, method, and areas of business affected, can be uncovered to prevent future occurrences. This, in turn, allows early detection to neutralize the damage of future insidious transactions.

Despite being labelled as one of the laggards when it comes to innovation, the banking industry can potentially gain the most from using predictive analytics to improve its processes. Predictive analytics opens up many doors for refining of banks processes, workflow, stakeholder management and many more. In the near future, it would not be unusual for businesses from other industries to take the banking sector as a case study for how innovation propelled rapidly, through fully leveraging the power of predictive analytics.

Find out more about best-practice innovation and big data analytics for the banking industry this October in Hong Kong. Hope to see you there!

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