Financial services companies benefit from adopting an ethical data strategy.
At first glance, it could appear as though the vast legal regulations and compliance standards governing the financial services sector would have data ethics covered off. But regulators aren’t the only ones paying attention to how companies manage data.
Financial services customers are basing their purchasing decisions not simply on products or services, but on a brand’s ethics, particularly in relation to how data is handled — and customers have a low tolerance for shortcomings. In its 2022 US Banking Digital Trust Benchmark, Insider Intelligence found that data policies could cause 48% of consumers to change their banking provider. When it comes to data breaches, customers are also more likely to blame the bank than the hackers. Suffice to say, it’s in the interest of financial services companies to consider their approach to data ethics.
What is an ethical data strategy?
An ethical data strategy is based on a set of principles, as opposed to laws or legislated rules. It adheres to moral concepts that are more difficult to substantiate, but are no less important, such as: justice, equality, respect, responsibility, honesty, quality, reliability, transparency and trust.
Given the intangible nature of these principles, it can be tough to know whether you’re adequately upholding them. In a McKinsey survey on AI, only 30% of companies recognized equity and fairness as relevant risks. To gauge the current state of your data ethics, it’s worth considering some basic questions:
- Is the data collected from trustworthy and appropriate sources?
- Is the volume of data collected reasonable and carefully selected?
- Are data points being used for appropriate purposes?
- Is the data analysis free of any bias?
- Are the results presented from data analysis accurate?
- Will the customer gain value from the use of their data?
More than just privacy compliance
Ethical principles go beyond simply following best data practices or privacy compliance standards. Privacy is primarily concerned with assuring users their personal information will not be misused or shared without their permission. Whereas an ethical data strategy covers all the processes involved in data management, from collection to access, analysis, and resulting insights.
Ethical data strategy also goes beyond the closed ecosystem of walled gardens, where the operator controls every facet of the data collected. While walled gardens limit access to information, making them a useful tool for providing data security, they don’t entirely address all the potential ethical issues around how the data may be used within the walled garden, including its accuracy or whether any resulting analysis is biased.
As data collection and analysis becomes more reliant on AI capabilities, financial services companies need to be increasingly vigilant about how unintended bias can impact results. Intelligence Insider found that a staggering 74% of companies have not taken steps to reduce unintended bias. It raises an interesting point about the need for algorithms to be taught ethics, and how to discern right from wrong.
Why ethics matter
Money affects people’s day-to-day lives in so many pivotal ways, it’s only right that financial services companies are bound by strict regulations, and those rules help ensure they meet the minimum expectations of security and protection within the industry. However, ethical principles pick up where regulations end, and can have impacts both internally and externally for an organization. The data a company uses and reports on can have far-reaching effects that impact employees, stakeholders and customers. It permeates into the culture of an organization, where regulations may not reach.
Yet when ethics are overlooked, the blowback can be fierce, resulting in a loss of customers, shareholders and revenues, not to mention the potential for the company or individuals to face prosecution. Globally, the average cost of a data breach in 2022 is $4.35 million. In the US, it’s over $9 million. If fines are imposed those numbers can swell into the hundreds of millions. More damaging, however, is the potentially irreversible impact on the brand’s reputation. Over 80% of consumers would potentially halt their online engagement with a brand after it has suffered a data breach. However, for companies that responsibly manage their response to an average breach, they could find appreciative customers boost their brand power by upwards of 29%.
When a company provides true and valuable information in all areas, from the quality of materials to the value of the product or service, to the price and promotions offered, it builds trust while also improving customer relations and solidifying the brand’s credibility.
Access versus accuracy
Suppose a company needs to decide whether or not to make an acquisition and is using AI capabilities to analyze the financial statements of the prospective corporation to predict profit forecasting and determine whether the investment is likely to be worthwhile. It’s all well and good to have access to the necessary data, but it must also be dependably correct. The company being analyzed may have supplied everything required, but is it all accurate? How do you know?
This highlights the difference between access and accuracy. While the company in question provided the regulated access to the required data, it’s possible it wasn’t being fully transparent or honest about the state of its finances. It’s a slight difference that can have substantial consequences.
Obstacles to data ethics
When it comes to embracing an ethical approach to data strategy, challenges are to be expected for financial services companies. Here are some of the main ones that tend to crop up:
Inefficiencies and errors
Transparency can be enlightening, in both good and bad ways. Increased data visibility can shed light on what’s not working within an organization, whether that arises as errors in judgment, poor decisions or, more troublingly, outright fraud.
Contrasting ethical priorities
This is almost a good problem to have. It occurs when data managers all hold some degree of ethical principles, they just apply them with varying levels of importance. For example, one manager might consider the reliability of collected data to be of utmost importance, whereas another favors accuracy in analyzing information to be more crucial. Getting both to see the value of the others’ stance is essential to keep a data effort on track.
Data source selection
There are various sources for gathering information within the financial landscape, but if the data sources are selected arbitrarily, it will be impossible to attain accurate analysis.
For financial services companies interested in adopting an ethical data strategy, there are a few things worth keeping in mind.
Display dedication and enhance brand awareness
Share details about the processes and policies you’ve put in place to support your ethical data strategy. Informing customers about your commitment to respecting their data helps build confidence in your brand.
Ensure financial information is trustworthy
Whether its balance sheets, income statements, profit and loss statements, cash flow statements, assets and liabilities, shareholders and beneficiaries or sales amounts, it’s vital that the data is comprehensible and accurate. Data-driven solutions can be developed within an ethical framework to review financial details and send warnings alerts when something seems amiss.
Be prepared to act on warnings
When ethical principles are applied to data analysis, it’s possible that disruptions may be identified in existing financial processes. For example, data mining techniques can reveal trends in manipulated data, such as management fraud that must be reported to stakeholders. These discoveries could carry the risk of economic loss for the business, so it’s important that companies understand that with adopting ethical practices, comes the obligation to act on them.
Just as ethical principles can cause data analysis to uncover fraudulent behavior, it can also be used to avoid potential pitfalls. Techniques can be applied to pre-emptively analyze uncertainties, such as credit risks that can help inform a business’ investment decisions.
Be clear about who can do what
2 steps to get started
Despite the current belt-tightening economic conditions, there are two steps financial services companies can take to start realizing the benefits of an ethical data strategy.
Lean into digital solutions
Data and user security technologies, such as firewalls, authentication and authorization, encryption, data masking, hardware-based security, data backup and resilience, and data erasure all have the same goal: keeping data safe and protected. Technology can help ensure an ethical approach with applications that support this culture. A composable approach can also help financial services companies modernize their tech stack in a manner that best fits their specific business objectives.
Bring your team onboard
Applying ethical principles in data science is almost more of a human endeavor than a technological one, making ethical data analysis part of an organization’s culture. All internal teams should be trained to adhere to ethical principles when collecting and analyzing information so results are free of prejudices and deviations.
Financial services companies should empower their employees and managers to observe ethical principles when accessing and analyzing user data. Being sure that user information is handled effectively creates security for the audience, encouraging brand engagement and building customer trust.
To learn more about how Appnovation can help you develop an ethical data strategy or advance your data maturity, don’t hesitate to talk to us.