Sensitive data is a pain for every financial institution. Especially in the case of AI-based automation and prediction. But does it mean there is no way to handle it? Check it now!
Every regulated entity, in particular within the financial sector, has specific obligations as too sensitive data – banking secrecy and/or professional secrecy. The reason why such institutions are subject to quite stringent rules (GDPR, PSD2, and others) is that the data they pose is highly sensitive and any data leak may lead to detrimental consequences, including financial losses for customers (individual authorization data) and/or loss of reputation. We can create more dark scenarios.
[Note: according to McKinsey ‘economies that embrace data sharing for finance could see GDP gains of between 1 and 5 percent by 2030, with benefits flowing to consumers and financial institutions’]
At the same time as such data is quite sensitive it also has a great value for both customers and institutions. Concepts like open banking and open finance are aimed at bringing more valuable services and ‘utilization’ of data. At the same time, according to SAS most of the banks are lacking data-driven insight that is a barrier to decision-making (only 19% of the respondent banks from the survey have indicated that they can forecast P&L for two years ahead). Customers are also more demanding than 10 years ago and regulators and supervisors are tightening the regulatory requirements. Finding the right balance in these difficult times may be hard.
This is why making use of data that the institutions have (both internal and external) is crucial for development and stability, especially in times of great innovation and rapid growth of the fintech sector. Without a more data-driven approach, many banks may lose their position in the market and get marginalized. But this does not have to be the case.
One of the most common reasons why banks are developing in the area of data and technology is lack of confidence in their external providers, fear of data leaks, and of course regulatory requirements, including outsourcing. Banks are not eager to share data (especially sensitive) to other parties and if so they expect to have robust frameworks for cybersecurity and operational resilience. Many have concerns and therefore do not move to another level with truly digital transformation.
As mentioned earlier, almost every AI-based tool (for predictions, recommendations, and so on) requires data of different types, including that data that has personal data inside. The good news is that technology and advanced methods, including natural language processing, are now able to accurately and effectively train on data that is anonymized without losing an appropriate level of effectiveness. The only “issue” is, therefore, to anonymize such data and put it in “motion”.
With such an approach you will be sure that no sensitive data is at risk of leak and will not be distributed to another third party. Your AI-based tool will not lose its main features and you will be able to use it even in the most “secret” cases within your organization. Sounds appealing? As your legal and security colleagues if they are satisfied and start your journey with automation and better data management. Profits will be visible soon.
