• Mark Smith, Head of Innovation Practice at Ayming


• 15.08.2019 06:45 am
Financial IT

Big data is AI’s big brother. While AI is driven by machine learning, big datasets fuel the intelligence engine.

Banks and insurance companies have access to huge volumes of data and are looking to harness this information to drive efficiencies in their businesses and generate personalised, targeted offers for customers.

Data is a high-value commodity, and data analytics is being used to mine its value across sectors, as with AI. “Big data is an incredibly profitable business, with revenues expected to grow to $203 billion by 2020,” according to Chris Neimeth, COO of NYC Data Science Academy.

Traditionally, data has tended to be stored departmentally within companies in data silos This can be limiting, as companies can have multiple pockets of data, potentially on the same client, without knowing which information is most up-to-date or joining the dots to complete a clearer profile of their customer.

Given the new practicalities of massive data storage, powerful analytics and more intelligent marketing, companies are turning away from the traditional methods of data collection and storage, and instead pooling big data within data lakes and warehouses.

This allows information to travel between departments, constantly updating, and each can see the bigger picture. Handled correctly, big data boosts efficiency and reduces companies’ operating costs.

But there are potentially ever bigger advantages. Insurance companies can make better underwriting decisions based on the wider breadth of data they access. Hedge fund investors can create more sophisticated trading models.

Banks too can identify opportunities to offer customers more tailored and timely products and services. Datadriven marketing, for example, means a retail bank seeing that a customer has booked a holiday, will know to offer currency deals or travel insurance. Alternatively, a customer with a higher salary thanks to a new job or promotion, is offered a suitable investment product based on their new earnings.

‘Predictive banking’ – offering an intelligent interface tailored to the customer’s needs – is a new buzzword for 2019. Spanish banking group BBVA, ranked top for online banking services in Europe in 201714, has developed a data-driven service for customers considering buying or renting property. Called Valora, it allows them to factor in approximate market values for their own and prospective homes and the impact on their finances of mortgage, insurance and other expenses – all from their cellphone.

Furthermore, Open Banking makes big data even bigger. The EU-driven move to increase competition allows financial institutions to access not only the data of their own customers, but those of competing banks as well. Banks may form a fuller view of new or prospective customers’ financial positions and make quick and personalised decisions on products and services that best suit the individual’s needs.

Big data, like AI, also plays an important role in fraud detection. Open access to data allows banks to better predict the patterns and behaviours of particular customers and therefore spot fraudulent transactions and prevent them from recurring.

Just as the freedom of Open Banking and data lakes make

accessing large volumes of data far simpler, they increase the complexity and the stakes, when it comes to processing, management and security.

Innovations around the responsibilities that come with big data, as well as the back-office and marketing opportunities, will continue to be a major focus of investment in the foreseeable future.