The Security Challenge in the Financial Services space – and the possible role of Analytics in Addressing it

“We shouldn’t ask our customers to trade between privacy and security. We need to offer them the best of both.” Tim Cook, Apple

Data is driving a profound change in banking customer experience (CX). Genuine concerns about data security and privacy resurface each time a major data breach or cybercrime occurs. Here are some revealing statistics about cybercrimes in 2020 and 2021:

• 86% of data breaches in financial services in 2020 were financially motivated.
• Data breaches exposed over 36 billion records in just the first half 2020.
• 70% of financial services firms rank cybersecurity as their top concern.

So, what are the leading data security challenges facing the financial industry in 2022?

A 2021 Deloitte report states that financial services are the leading industry targeted by hackers out of around 26 industries. Adding to that, 47.5% of financial companies were breached in 2019, while 58.5% have witnessed a potential attack or suspicious activity.

When it comes to data security, here are the top 5 challenges faced by the global financial services sector:

The 2021 State of Ransomware in Financial Services report states that a ransomware attack hit 34% of financial services companies in 2020, while 51% of the organizations reveal that attackers succeeded in decrypting sensitive data.

Cybercriminals view this industry as a source of “high financial returns” for their efforts. The high liquidity level in financial firms encourages hackers to continue their efforts.

Thanks to globalized financial services, massive exchanges of sensitive financial data across international borders add to the data security challenges.

A recent Akamai report on cybersecurity found that malware attacks like SQL injections, XSS attacks, and LFI were used in over 90% of the cyberattacks in the financial services domain. Apart from malware attacks, insecure third-party tools that promote customer convenience in the financial services sector also add to the data security risks.

Human (or accidental) errors also increase the risks of data insecurity as more “remote-working” employees access company networks from home or other insecure public places. In such changing circumstances, banking networks and applications lack the security infrastructure to keep “smart” hackers away from their sensitive data.

In recent years, the traditional banking sector has faced increased competition from non-traditional Fintech companies and new entrants. The financial industry is also facing growing challenges in customer service as customer expectations change rapidly.

Changing customer expectations, especially for a personalized CX, is increasing the adoption of the latest digital technologies as customers demand faster access to products and services. To retain customer confidence, banks, and financial service companies can cut corners on security to reach their consumers with new products and offerings.

Industry regulations like GDPR and CCPA have emerged to protect customer interests and prevent data-related fraud in recent years. Non-compliance with these regulations means that banks and financial service companies can now be penalized with hefty fines causing a loss of reputation.

To comply with these regulations, financial companies now try to deploy various techniques, including data encryption, tokenization, and masking, to secure their sensitive data. Frequent data and knowledge sharing with third-party vendors and companies add to the risk of the data getting stolen or leaked due to human error.

Banks and financial service companies are adopting various digital technologies to stay relevant in a competitive industry. While digital technologies like Conversational AI and RPA are beneficial, they also act as a “double-edged sword,” as improper execution can be a security nightmare by providing greater surface area for hackers and cybercriminals.

Further, emerging technologies in the financial domain, like Blockchain and IoT, need more industry regulations and security standards, making it challenging for the industry to mainstream.

Can data analytics help in overcoming these challenges? Let us discuss this next.

Thanks to the continued adoption of mobile phones and IoT devices, there has been exponential growth in the volume of customer-specific data across all industries. Among the most data-intensive industries, the banking industry has leveraged significant benefits from data from touchpoints like PoS terminals, ATMs, online payments, etc.

Equipped with the ability to tap this wealth of data, how can effective data analytics help improve data security in the banking and financial services domain? Here are a few points:

Effective data analytics enables timely detection and prevention of financial fraud and data breaches. Overall, analytics plays an effective role in customer risk management by identifying high-risk customers and adding levels of verification to their banking accounts. On its part, AI and machine learning technology in customer analytics can detect suspicious changes in customer behavior and data transactions.

Typically, any bank determines its consumer’s creditworthiness through their credit score or the history of their financial transactions. With data analytics and individual monitoring, customer data can now be deeply analyzed for credit risk analysis and determining the overall risk of granting a loan to these consumers.

By building efficient data models with analytics, banks, and financial institutions can perform risk modeling to offer safe and sophisticated services to their customer base. Based on historical customer data, data analytics can minimize business losses and predict the degree of involved risk when dealing with individual customers.

By automatically monitoring their customer’s credit exposure, financial services companies can use data analytics to develop more efficient credit policies and strategies for potential customers.

Typically, liquidity risks are caused by insufficient funds caused by bad loans or low cash flows in financial accounts. This can further lead to banks defaulting on their loan repayments. On its part, data analytics can be useful in the timely detection of situations where banks could default on loans and take corrective measures to prevent the consequences of high liquidity risks.