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Digital Transformation

The Ethical use of big data in financial services

by Mike Jones in Data Analytics 12/08/2021

As the industry transforms, there will be winners and losers associated with the fundamental changes in the finance industry as a consequence of big data. Rising competition, combined with the breaking down of traditional measures replaced with alternative systems, will impact the businesses we see today.

Customers will likely benefit from enhanced products and services, and businesses will be more capable of managing risks and overall efficiency. However, there is the potential that some customers may be excluded from selected markets and will inevitably experience exposure to new risks.

In a recent study, the Institute of Chartered Accountants in England and Wales (ICAEW), explored how the finance industry has changed because of the increased availability of new data. Unlike other businesses, employees in the financial services industry will experience the value of big data. While customers can decide which companies to purchase from, financial services are a critical part of our daily lives. The collection and use of data are valuable to the industry, meaning there needs to be careful consideration of the ethics involved.

The growing reliance on data enables customer behaviour to be shaped by digital technology, which feeds into the supply of data. Plans and decisions regarding big data have a direct social impact across the entire finance industry. As customers become more sensitive and aware of data in business, financial service providers will have to ensure they maintain their responsibilities. 

Several principles can support financial businesses with managing and resolving any ethical tensions.

Accountable for Big Data

Big data and new technologies are increasingly important to businesses today, but industry experts have pointed out that many organisations lack the skills and experience in this field. The complexity of these technologies must factor in as this understanding is vital in determining strategy, company values and culture. A chief data officer should be recognised as an asset within the senior leadership team to ensure accountability within an organisation and with regulators.

Business Management in the era of Big Data

Finance businesses are often complicated, running from a range of systems. Operating normally in this new world of big data will present new challenges that will require time, investment and new resources to help businesses prosper. Despite its significant rise, big data isn’t something for the finance industry. An element of the transition to big data will be focusing on existing data and using this information in new and innovative ways.

As the industry progresses, businesses must show that they have analytical capabilities, appropriate measures and suitable data storage facilities to use big data properly. Without these core elements, they will potentially be at a competitive disadvantage and at possible regulatory risk from generating inaccurate findings that could result in unfair treatment to customers. Having higher volumes of data also poses potential issues of cyberattacks.

Treating all customers fairly

All financial companies must be capable of consistently showing that they implement equal treatment with their customers. As companies evolve with the growth of big data, organisations will have to determine how they interpret fairness and how they intend to keep customers at the core of their business.

If a business relies on historical data, it may generate bias created by how information is collected. This trend can continue and be reinforced in areas where there are gaps in data. This can lead to ethical issues of using data for customer decision making and product design. Customers need to have a clear understanding of the impact of making a selected big data decision against them. Businesses should then notify customers why decisions were made and include the relevant criteria. The customer can then determine whether the information is correct or not and provide corrective support. This type of feedback mechanism offers more transparency to the customer and enhances data accuracy for the business.