Author: Mubarak Musthafa

The financial sector is one of few industries involved in producing massive amounts of information through daily records, second only to telecommunication and possibly E-commerce. They deal with numbers so large that it sounds odd even to say! (try “quintillion” or “zettabytes”) These records are typically in the form of transactions and stored in numerous, large digital databases.

As these records are technically known, all these data are rudimentary pieces that comprise a complex collective called “big data.” When coupled with technology such as machine learning, the internal processes of the user (in this case, the bank) can be significantly optimized to cut costs, personalize client experience and mitigate risks.

What is the Role of Big Data in Banking?

Banking processes alone are capable of producing an unfathomable quantity of information. All this information must be organized and handled efficiently to keep operations from falling apart by the end of every working day. This very Herculean task is accomplished by manipulating the various types (structured, unstructured, or semi-structured) and flow (variety, velocity, and volume) of big data to optimize operations.

Big data primarily allows for customer self-service, which is one of the forefront merits of the digitization of banking. Personal and on-site communications between bank and client are no longer required. In this way, most banks can attain all necessary information regarding an interested or current client, including spending patterns, risk profiles, personalization strategies, motives for feedback online. The nuanced analysis of big data allows all this information and more to extract from a hoard of financial data for every individual customer, thus providing a detailed picture of the industry.

6 Ways Banks Can Use Big Data to Enhance Their Products and Services

As mentioned above, big data can be classified into three distinct types and flow patterns. Structured data is highly organized and fixed formats, while unstructured data does not have a general structure. Semi-structured data bridges the gap between these two types by consisting of keywords capable of processing the seemingly unstructured data. Either or all of these types may be categorized by their sheer number (variety), the digital space they occupy (volume) or the frequency at which they are added to the database (velocity). For instance, 100 distinct transaction details per minute may consume a few gigabytes of space, thoroughly describing the data involved.

In the following segments, we will see how a bank uses such information to the advantage of internal operations with relevant examples:

Customer segmentation, profiling, and targeting

Banks can analyze their volume of big data to evaluate customer behavior patterns. This allows them to group clients into standard filters according to their financial habits in order to provide more individualized experiences and financial advice (i.e., customer profiling). For example, thrifty spenders that live from paycheck to paycheck would be offered a detailed income management plan to ensure financial security, and cautious investors would be counseled on investments best suited to their financial habits.

Personalized marketing

Various big data models can be used to forecast customer requirements and predict the most desirable customer interactions. A range of different audiences can be targeted using specific marketing campaigns to encourage personalized engagement instead of using a single, general campaign to reach a varied demographic. To do this, banks may opt to process external information about their customers alongside their internal analyses to make an educated decision.

Fraud detection and prevention

Understanding a client’s spending pattern can help prevent attempts to fraud or take unethical advantage of the bank. Fraudulent behavior both affects the customer personally and holds the issuing bank legally liable for any unauthorized clearances. These issues are common with stolen or misplaced ATM cards, for instance. In such a case, the bank may recognize unusual transactions that have never been observed in the cardholder prior and hence take actions accordingly.

Lowering investment risks

Financial advice to customers on investing in stocks or asking for loans can be delivered with great confidence by analyzing objective patterns such as credit scores and debt payment. These patterns can be processed using various big data algorithms that provide a detailed client’s detailed financial history. For example, a bank may look into a customer’s salary growth or decline over a period of time to conclude whether a timely repayment of a provided loan would be justified.

Identifying opportunities for upselling and cross-selling

Cross-selling or upselling peripheral offers and products can help the bank better engage their customers and improve the overall customer experience. They may propose complementary or supplementary recommendations in conjugation with the base product to support the customer. In continuation of the examples above, the bank may provide the cautious investor with better ROI (return on interest) options to get them to spend more actively and the thrifty spender with low sum, long-term loans to get them to decrease their financial burden.

Customer feedback analysis and application

Big data is not limited to daily working operations and may contribute to adjusting the nature of operations as a whole as well. Various big data tools can look through large volumes of user feedback and general opinions to identify trends and better understand what customers look for in their banking experience. This allows the bank to nurture healthy customer relationships, dramatically increases customer compliance, and helps raise publicity towards their brand. Valuing the customer’s opinions comprise an excellent marketing strategy while ensuring full reciprocity from the user.

Advantages of Implementing Big Data in Banking

In conclusion, implementing big data and its related streams into the banking sector provides significant and obvious benefits, as we have come to know. It provides a complete view of the business, allows decisions to be made on optimizing internal processes, and reduces the risk involved in dealing with numerous customers. It also enables the bank to better connect with their clientele and reduces the likelihood of fraudulent activity. The insights gained from analyzing customer behavior can also help forecast customer requirements, increase satisfaction, and promote positive publicity.

In the realm of digital data management, big data has proven to be a boon to information-intensive businesses everywhere.

Over the last decade, cyber-attacks have grown so popular in the banking sector that it is now considered one of the industry’s biggest threats. Cybercriminals have evolved in technology – enhancing their techniques and skills, making it very difficult for any financial service company to top the threat each time. Cybersecurity is not limited to any industries involving technology. It holds a critical value in banking since banks make millions of transactions each day, and most of them done on digital payment platforms. This makes the banking sector the primary target for cyber attackers all over the world. 

The importance of cybersecurity to the banking sector

Protecting customer assets is one of the biggest reasons why cybersecurity is essential in the banking industry. Since the internet is taking over the world, most people are going cashless, and activities involving money are done through credit card scanners and online checkout pages. In both cases, the PII of the customer can be taken or redirected to any location and can be used for malicious activities. These activities will affect the customer and hurt the bank when they try to recover the data. 

Moreover, banks might end up paying hundreds and thousands of dollars to cyber attackers to release the information when their data is taken hostage. This will affect the bank financially and affect its reputation since the customers will lose their trust in the financial institution. Since the world sees a wave of digitization, banks need to upgrade their cybersecurity since hackers might quickly get through these walls. These reasons clearly show why cybersecurity is essential for any financial institution.

What will be the impact of a cybersecurity breach in the banking sector?

The possible losses banks might face as a consequence of cyber-attacks on their platform are:

• Cyber attacks can cause substantial financial losses for the customer as well as the banks through false transactions.
• Attackers might sell the confidential information they steal from the banking institution. The stolen data is later used for malicious purposes.
• Hackers may begin targeting specific customers that are part of the organization. This may result in customer identity theft or customer frustrations.
• The public image of the financial institution will be damaged for insufficient information security compliance. 

What are the modern cybersecurity threats to the banking sector?

While there are many common cybersecurity threats to the financial sector, a few modern threats that have been emerged recently are:

Identity Theft

Identity theft is the practice of taking someone else’s financial or personal data without their knowledge with the motive of conducting concealed, illegal activities. When there is a privacy breach in a bank, the stolen information of the bank’s customers is usually sold and purchased on the dark web by illegal organizations and other cybercriminals. 

Spoofing

This type of cyber threat is relatively new, in which the cyber attackers find a way to imitate the bank’s URL with a fake website that functions and looks exactly alike. If attackers are able to convince the customer to open and use the fake website instead of the real one, the customers usually submit sensitive information such as username and password thinking that they are in fact logging in to the bank’s real website. This information submitted by the customer on the fake website is then quickly taken by hackers to be utilized or sold later.

Insecure Third-Party Services

Several financial institutions use third-party tools such as chatbots and customer relationship management software offered by other private companies for providing their customers with a better service. However, if these third-party companies are not cyber secure, it can impact your bank. The level of integration that such third-party tools have to the core banking systems and to the systems where customer data is stored should be monitored and audited carefully.

What can banks do to ensure their banking institution is cyber secure?

The number of cyberattacks in the banking industry is increasing, but there are several ways to minimize the risk of getting hacked.

The right security solution

Manually monitoring every transaction requests to check for possible threats is impractical. Therefore, banks will have to switch to using a process automation solution to automate all monitoring and scanning processes, block and filter malicious traffic from getting into the network. An ideal security solution will stop and screen suspicious activities and track patterns of these attacks and work accordingly. Moreover, these cyber solutions must be easy to customize and configure.

Educate Employees

Making employees aware of the threats that are commonly faced by financial institutions is essential for preventing most of them. Since many of the cyber attackers targets employees and try to extract information from them, awareness of such activities and what to expect needs to be made aware to the team. For example, all employees working in the banking institution must be aware of the risks of downloading or opening email attachments that come from unknown or unreliable sources. This is particularly important since many cyber attacks are from viruses that come through these attachments. Banks must also prohibit their employees from sharing confidential information.

Audit all systems regularly

Cyber attacks can always be traced back to some loophole or minor vulnerabilities in the internal systems and network setups. 

Look out for anything such as missing security rules, development bugs, misconfigured systems, or outdated extensions, since these might end up being the loopholes that hackers later take advantage of. Vulnerability assessments can help identify lagging infrastructure on the security section and correct them. At the same time, penetration tests can simulate real-life cyber attacks to test the strength of the security system.

Conclusion

The banking industry has so many opportunities to improve its cybersecurity despite the vulnerabilities. Financial institutions can focus on finding solutions to cyberthreats and implement those solutions to their networks while adopting newer technologies that can provide a seamless banking service.