Data privacy

Data privacy governs how institutions and organizations collect/gather, share, store and delete personal data. In other words, data privacy safeguards user’s rights to private preferences.

There are laws established to ensure compliance with data privacy for example General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA).

In most cases, the laws can be narrowed down to address specific needs e.g of a particular sector. In the financial sector, there is the Gramm Leach Bliley Act which regulates how all financial institutions should notify their customers about their data-sharing practices with other third parties. Here is how automated DQ tools help the banking sector achieve data privacy.

Data privacy in banking

Today, data is fundamental to the success of almost every sector, banking included. It is needed for innovation and advancement. However, it is also keen to note that upon misuse, it can lead to devastating losses and destabilization of systems.

Banks are known to have a lot of sensitive data on their customers. After the 2008 global financial crisis, regulators strived to improve transparency in financial institutions. After that, there have also been other incidents, for example, the 2017 data scandal on Equifax as well as the 2018 scandal on Facebook-Cambridge Analytica.

These recent incidents popularized the need for data privacy for customers. Customers are keen to know what data about them is collected, its use and which other parties can access the information. Having data privacy policies that are forthright on the usage, storage and sharing with other parties available to them before data collection greatly improves their confidence with banks.

Confidence is key since most bank customers use their cards or transact from their bank accounts with other parties trusting that the bank safeguards their information from malicious third parties.

To ensure compliance with data privacy in the banking sector, banks need to have a proper understanding of the kind of data they need from customers, especially personal data. Once they have clearly identified this, they need to also strategize on how to collect/ access this data. That is if they can source from other third parties or collect it themselves directly from the customers. The latter is mostly preferred since banks are able to verify their accuracy. Choosing this option requires them to expressly seek consent from the customers before collecting the data. This request on consent is mostly in the form of data privacy policies which also outline the use of the data.

Regulators have also become more vigilant on how organizations handle data, especially personal data. They are constantly rolling out measures and approaches that are flexible enough to adapt to the new advancements happening every day.

Nowadays, for overall economic growth organizations need to cross share information available to them about the same customers. For example, when carrying out due diligence on a customer, a bank will need the credit history of the customer from credit unions. The need to share data can also arise from organizations outside of the sector for example IT.  The Payment Services Directive (PSD2) now allows large technology firms gearing towards financial services to access customer data by integrating their systems to those of banks.

The benefits of automated data quality tools

You cannot mention data privacy without having to talk about data quality. Data quality is the measure of data to meet its usefulness by being accurate, complete and consistent to meet its intended uses as well as reliable and up to date for use in planning and influencing decision making.

Banks are known to have an extensive collection of their customers’ data. Talk of personal data collected for due diligence, all withdrawals as well as deposits, all purchases (whether done online or at POS), any applied loans, mortgages, credit history, etc. It is therefore in the best interest of the banks to make proper use of all these data sets to improve their offerings as well as have a competitive advantage over other financial providers.

This is made possible through the use of automated data quality tools. Most of these data quality tools are in the form of automated technological innovations and processes that carry out crucial functions such as to enrich, monitor, profile, clean, standardize, match and parse data to contribute to informed decision making.

1. Improved customer experience

With automated data tools, banks are able to have a 360-degree customer view. This way, they can profile the customers and create customized products for them based on their preferences and needs. This saves time for customers as well as improves their experiences leading to long-term customer retention.

2. Curb fraud

Fraud, money laundering and identity thefts are some of the biggest challenges facing the banking sector. The Federal Trade Commission reported 1.7 million fraud complaints in 2019. We mentioned a critical function of automated data quality tools is monitoring. Through this function, banks are able to monitor and single out unusual patterns that can help prevent fraud.

3. Compliance

Regulators have put in place laws and regulations on data. These encompass data quality. Failure to be compliant can lead to revoking of trading licenses, punitive penalties and court cases. The use of automated data quality tools in banking sector to handle data ensures compliance with the said laws preventing adverse effects on the flow of business and ensuring continuity.

4. Lower operating costs

One cannot compare the cost of having automated data quality tools to that of correcting huge mistakes/losses caused by lack of DQ tools. For example, if a bank does not correctly profile its customers, it may end up having so many defaulting on credit services advanced to them beyond their repayment ability.

When decisions such as creating new products or marketing are based on well-analyzed data, their likelihood of success is very high translating to saving costs of not getting it right the first time.

5. Increased returns

Almost all the benefits mentioned above will in the long run contribute to increased returns to shareholders. Correct marketing strategies, improved customer satisfaction, accurate financial projects, etc all lead to improved revenues and reduced operational costs hence overall increased returns to shareholders.

6. Informed decision making

With new innovations and new product offerings arising every day, banks need to have the ability to adapt easily and super fast to any changes happening. This means decision making should be very fast and well informed. Data is the key driver in making decisions. For this reason, this data needs to be properly analyzed to give the most accurate insights. This is only possible with the use of DQ tools such as AI and machine learning.

Summary

In the banking sector, data privacy goes hand in hand with data security but differs in definition as well as application. Data security involves protecting an organization’s data from malicious access or attack from third parties. An organization can have elaborate measures to ensure data security but fail on data privacy. For example, they may have collected the data using unlawful methods.

Therefore, data security can exist without data privacy but adherence to data privacy requires proper measures on data security.

It is not only very important for banks to ensure proper data privacy measures are in place, it is actually mandatory for them to meet the regulatory requirements stipulated by applicable laws.

Data requirements especially for banks and other financial institutions keep on evolving and becoming more stringent. This is in a bid to enhance data privacy for users to avoid manipulation or wrongful use of any information they provide especially without their consent. However, regulators should be careful to find a balance between too stringent regulations and customer data safety. Over-regulation can inhibit innovation of new products and services or customized needs beneficial to the customers.

Most customers are willing to share their data freely in return for improved and personalized services but they at least want to be informed prior how much of their personal data is needed and who it is shared with.