The Security challenge in the Financial Services space – and the possible role of Analytics in addressing it

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

 

Data is driving a profound change in customer experience (CX) in banking. 

Having said that, genuine concerns about data security and privacy resurface each time there is a major data breach or cybercrime. Here are some revealing statistics about cybercrimes in 2020 and 2021:

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

 Top 5 challenges in data security

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:

 1. Data breaches and losses

The 2021 State of Ransomware in Financial Services report states that 34% of financial services companies were hit by a ransomware attack 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 level of liquidity in financial firms serves to encourage hackers in continuing their efforts.

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

 2. Malware and cyberattacks

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 used to 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 do not have the necessary security infrastructure to keep “smart” hackers away from their sensitive data.

 3.Changing customer expectations

In recent years, the traditional banking sector is facing increased competition from non-traditional Fintech companies and new entrants into this sector. Adding to that, the financial sector is also facing growing challenges in customer service, as customer expectations change rapidly.

 Changing customer expectations, especially in terms of a personalized CX is increasing the adoption of the latest digital technologies, as customers continue to 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.

4. Industry compliance and regulations

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

 To comply with these regulations, financial companies now make the effort to deploy a variety of techniques including data encryption, tokenization, and masking to secure their sensitive data. Adding to that, 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.

5.Emerging technologies

Banks and financial service companies are adopting a range of 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 have little to no industry regulations or security standards, making them challenging for the industry to go mainstream with them.

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

 How can Data Analytics ensure Data security?

Thanks to the continued adoption of mobile phones and IoT devices, there has been an 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 available from touchpoints like PoS terminals, ATMs, online payments, and so on.

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

  • Detection and prevention of financial frauds

Effective data analytics is enabling timely detection and prevention of financial frauds and data breaches. Overall, analytics is playing 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. 

  • Credit risk analysis

Typically, any bank determines its consumer’s creditworthiness through their credit score or 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. 

  • Risk modeling

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.

 Data-backed credit policy

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

  • Managing liquidity risks

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. 

Conclusion

In the financial services domain, data encryption and analytics can play a critical role in overcoming challenges posed by data security and changing customer expectations. With banks having access to large volumes of customer data, analytics is the best way forward to deriving valuable insights and preventing cyberattacks.

 

At Ellicium Solutions, we focus on enabling business transformation for our financial domain customers through our expertise in Big Data, Analytics, and AI technologies. Read some of our best customer success stories on this page. Get your data journey started today by dropping us a message.