The original version of this article was published on Mostly AI Blog
Looking back at data-driven innovation in banking
Looking back at my years at a retail bank that involved developing new digital offerings, I often remember hearing the proverb “You can’t have your cake and eat it too.” Trade-offs are an integral part of innovation in a heavily regulated and risk-averse environment like banking.
Normally, you start with a really exciting idea. This later gets watered down by a series of trade-offs (security and legal requirements, infrastructure limitations, etc.) which then results in a super-secure and compliant outcome that fits perfectly into a bank’s infrastructure. But the offering itself is often “dead on arrival”. Hence, a large part of my job at that time consisted of applying lateral thinking methods: finding ways around the internal limitations in order to keep innovations alive.
Some of my most exciting projects back then were data monetization projects. We were asking ourselves how could our bank unlock the untapped utility hidden in credit and debit transactional data and transform it into added value for our private and corporate customers. The sky seemed to be the limit. But very soon we realized that the biggest limiting factor was much closer to earth. In fact, it was as close as the office of our bank’s data protection officer.
Innovation vs. Privacy
As you could imagine, a customer’s data is subject to a stringent data protection legislation. I’ll use GDPR as the most popular data protection regulation to explain the major challenges of data-driven innovation in banking, but using other regulations wouldn’t make a big difference to my arguments. Let’s look at three major privacy-related stumbling blocks that you have to get around if you want to succeed with your data-based innovation:
1. Legal basis
Everything starts with a legal basis for processing customer data. Most data-driven innovations require a previous customer’s consent. In addition to explicit consent, there are some other legitimate reasons for data processing, such as the “need to process the data in order to comply with a legal obligation”. But don’t expect that your idea will fall into one of those categories. Getting customer consent for data processing is a herculean challenge on its own. It’s essential to make sure that customer consent was given freely, specific to the data usage intent, informed, unambiguous, and able to be revoked at any time. Even the best marketing campaigns won’t get you more than 15-20% approval for your new service from your target group. Many predictive applications require a critical mass of data subjects, whose data are feeding the algorithms. Therefore ask yourself early whether you will be able to get a sufficient number of customers to consent.
2. Security and Confidentiality
With the legal basis fulfilled, a step not many ideas overcome, the next challenge awaits you: secure processing. The processing itself must guarantee appropriate security and confidentiality of the personal data. This is usually a mix of different security measures of data protection involving data access management over encryption to data anonymization – the seemingly magical solution that transforms personal data into non-personal, free to use data, as it falls outside GDPR’s scope.
These security and protection measures are a necessity for all phases of product development that require customer data. Data has to be protected throughout the product life cycle with permanently changing product features, product-related infrastructure and up/downstream applications. A compelling argument for privacy and data security “by design”, as building strong privacy and security processes into your new data-driven product right from the very beginning allows keeping the effort to a minimum – even in a constantly changing environment.
3. Public perception
However, having a legal basis for data processing and even privacy and data security by design doesn’t offer absolute protection against a public outrage. If your new product or service is perceived as unethical, manipulative, reinforcing biases and disbalances the power between the bank and its customers it will not take much to fail upon arrival. Mark Zuckerberg famously said in 2004: “You can be unethical and still be legal; that’s the way I live my life.” This type of thinking might be an option for a company like Facebook but it is definitely not an alternative for a bank that needs to be trusted by its customers
Sometimes it’s not even the question of ethics. One inept communication can destroy the trust you spent years building. Letting public imagination run wild with privacy related topics can end in a disaster like what happened with this Dutch bank in 2014. Faced with public outrage, the bank had to refrain from its initial intention to use customers’ spending habits for targeted ads.
Required by law or not, be transparent, ethical, and aware that more and more of your customers are becoming data and privacy literate. Are the short term gains of an insight worth the collective trust of your customers? Don’t forget that data privacy is a very sensitive topic that requires extremely clear communication and an in-depth ethical review.
No lateral thinking will get you around privacy and data security requirements. Any mistake in the privacy design of your data-driven innovation could lead to a violation of data privacy legislation or, even worse, put your bank’s most valuable asset at risk: customer trust.
Should you just give up being innovative?
There’s no “fail fast” in data privacy matters. There are so many moving parts, so many juggling balls to keep in the air concerning privacy and data security topics that at this point, most of the innovators within the bank will simply give up. Under those circumstances, you often have to choose between data-driven innovation and data privacy. You can’t have your cake and eat it too. Or can you?
More and more organizations are starting to understand that there is significant commercial value behind the ability to protect customer privacy. Because only customer data that can be used, shared, and monetized in a privacy-compliant way is commercially valuable data. Over the last few years, I’ve observed an entirely new market quietly developing with the goal of achieving the state-of-the-art privacy protection modern data-driven organizations require to operate and innovate with their customer data.
New privacy approaches enable innovation
The three most promising new privacy approaches in this market are differential privacy, homomorphic encryption and AI-generated synthetic data.
Differential privacy is an abstract mathematical framework for privacy to find the maximum influence any single person can have on a given database query, algorithm or any other statistical method to quantify privacy risk for individuals in the data set. In recent years applications of differential privacy have been used by tech giants like Google and Apple to run analytics on private data. Those applications faced some criticism from the privacy research community because they don’t reveal how they calculate differential privacy and some researchers even argue that companies like Apple even sacrifice some privacy in order to increase data utility.
Homomorphic encryption is a class of encryption methods that enable computing on data while the data is encrypted. The data stays encrypted during the entire process of processing. The secret key doesn’t have to be shared with the entity that processes the data. The output of processing remains encrypted and can only be revealed by the owner of the secret key.
AI-Generated Synthetic Data
One of the most fascinating technologies in the PrivacyTech market is AI-generated synthetic data. It is “fake”, artificially generated data that is based on a given real-world dataset. But even though synthetic data accurately resembles its real counterpart and its statistical properties, it does not include any customer’s actual information. Thus it is fully anonymous and exempt from GDPR.
To sum it up, these three new privacy protection techniques finally allow banks to leverage the utility hidden in customer data while at the same time providing the highest level of privacy protection. Privacy protection can’t be used as an excuse to not innovate with data anymore. Innovations in privacy protection are the key to fixing data-driven innovation.
This was the first part of a mini-series on PrivacyTech in banking. But before diving deeper into new privacy protection techniques and especially my favorite – synthetic data, in the next part, I will explore the shortcomings of classical anonymization techniques frequently used at banks and the consequences involved. Your anonymized customer data isn’t as anonymous as you might think it is. Stay tuned…