The Big Data Challenges Facing Financial Services

Many financial services companies have had access to huge volumes of new data as a result of their digital transformations, but they have only just begun to take full advantage of the wealth of material at their disposal.

The average business only collects and analyzes 24% of the operational data it has, according to a global survey conducted by Seagate and International Data Corporation in 2020. There are several reasons for this, ranging from concerns about customer privacy and regulatory compliance to the difficulties of getting all the relevant material in the right format to crunch it.

The first big challenge facing financial services companies looking to use their data more and better is “the data ecosystem, the second is talent and the third is data management”. This is the opinion of Edouard Legrand, Chief Digital Officer of BNP Paribas Asset Management.

The elastic and nearly limitless capacity of the cloud helps address the first challenge. The cloud-based technology has also introduced many new features for data and analytics teams. But Legrand believes that “while progress has been made in data mining, processing and governance, there is always room for improvement. Emphasis has been placed on implementing cross-functional platforms that allow everyone in the organization to get the most out of data. Ultimately, all information should be transparently available, as should the tools to exploit it. »

When Legrand talks about the talent challenge, he is referring to the industry’s inability to attract enough people with the required IT skills.

Attract talent

Nick Broughton, Chief Information Officer at Novuna, agrees with this assessment. “Technology alone does not generate value; you need people who know the data and have good ideas,” he says. “Data science skills in particular are key to getting real insights from the wealth of data we have. Attracting, retaining and developing our internal talent pool around these new skills is an additional challenge when the demand for them in our market is so high.

A survey of more than 250 financial services companies in November 2021 by recruitment giant Hays found that 83% had difficulty recruiting data scientists, even though they typically offered annual salaries of over $100. £000 for such jobs. More than a quarter of respondents said they did not have all the skills needed to achieve their business goals.

The sector will need to become more flexible in its employment policies and practices if it is to attract and retain much-needed data scientists. Knowing that they are in such demand, these professionals can dictate the terms. Many prefer to work from home and not on the usual nine-to-five schedule, for example, so it’s up to employers to take that into account.

Technology alone does not generate value; you need people who know the data and have good ideas

Some companies go to great lengths to establish a reliable pool of talent, such as building relationships with data science communities and setting up apprenticeship programs.

When it comes to meeting the third challenge cited by Legrand, data management, companies strive to put in place more effective governance systems. Their general objective is to provide a global vision of their products and their customers thanks to the data collected and processed in real time. Making analytics tools usable and easily accessible to everyone in the organization who needs them is critical because no one wants to call the IT team every five minutes.

One of the main opportunities arising from all this work is that it helps companies find new ways to delight their customers, according to Broughton.

“Creating exceptional customer experiences should be at the heart of any data-driven initiative,” he says.

The role of AI

Providing ever more personalized services is an obvious area for development, but the potential for augmented advice and services, where data insights complement human interactions, is also exciting some companies. By combining multiple data sources, for example, they can generate investment signals and insights that relationship managers can use to better inform their clients.

With the help of artificial intelligence technologies such as machine learning systems, customer-facing employees can identify patterns and trends that they could never spot without help. Smart tools can also help clients better understand their financial health and the risk/return profiles of the investment opportunities available to them.

“We see a future where customers can use tools to experiment. AI models can show them how investments might change over time, for example,” says James Brake, acting chief data officer at Hargreaves Lansdown. “Yet we find ourselves in a world where regulation often lags behind technological innovation. With this in mind, financial services firms need to be careful in their approach to AI-enabled services, for example, to maintain customer trust. That’s why Hargreaves Lansdown recently created the position of Data Ethics Officer. This helps ensure that our approach to AI is transparent, repeatable, unbiased, and capable of delivering the best results for clients.

Financial services firms are already using virtual assistants and chatbots powered by natural language processing, an AI technology that is fast becoming standard fare. Natural language processing also allows them to perform automated searches of information sources ranging from news feeds to earnings reports, which will quickly identify potential issues such as profit warnings and greenwashing cases. .

The use of alternative data sources – for example, satellite cameras and smart sensors for climate-sensitive investments – is also likely to become a standard way to inform performance forecasts, for example.

Open banking with data sharing is another exciting area to watch.

“As the world moves towards thinking about data as a product, we need to start building our services with the data they serve in mind,” says Broughton. “Making our data products more interoperable, secure and governable will be crucial to the success of future initiatives in this area.”

Once the data becomes the product, expect to see a whole new ballgame.

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