Big Data for Fraud Detection in the Banking Sector

Big Data for Fraud Detection in the Banking Sector

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Executive Summary

The report proposes the analysis and evaluation of big data and the way it can be of benefit in fraud detection in the banking and financial services sectors. It will assume an analytic approach to how data can be harnessed through collection procedures as well as storage to allow for analysis and generation of past trends, the current situation, and the prospective trends of fraud in the financial sector. The report will also analyse how the collected data can be used for business development concerning the creation of consumer-centric products as well as the generation of a recommendation engine allowing users to give a rating to the products. For business continuity, the power supply is critical. A power outage can interfere with the normal operations of the business. In some instances, a power outage can trigger loss of crucial data in a case where there is a weakness in the storage infrastructure, for instance, loss of crucial customer details (PricewaterhouseCoopers, 2014, p.2). The report will demonstrate how businesses can survive in case of an outage. Results from data analysed indicate that banks and financial service providers remain a key focus for fraudsters. Asset embezzlement remains the basic form of reported financial crime. Emerging trends that are on the rise include corruption, bribery, and cyber crime. The report finds that fraud can be detected using big data allowing for the formulation of the appropriate aversion measures. However, for big data to provide a solution, mechanisms have to be in place for collection, the collected data has to be stored in secure, cost effective, and easily accessible systems (PricewaterhouseCoopers 2014, p.2). Since fraud and crime are constantly changing, the data management systems assumed by banks and financial institutions have to be dynamic enough to accommodate this dynamism in fraud and crime (PricewaterhouseCoopers, 2014, p.3). The systems should be in a position to capture, store, and disseminate emerging trends.  

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Big Data for Fraud Detection in the Banking Sector

Introduction

According to PricewaterhouseCoopers (2014, p.3) report, 45% of financial institutions have been victims of economic crimes. The figure is slightly higher than the 34% experience level across all other sectors during the same period (PricewaterhouseCoopers, 2014, p.3). Fraud can take different forms, and these forms may include: money laundering, bribery, corruption, and anti-competition practices.

However, analysis of big data provides relief, helping banking institutions to understand customer patterns as well as the industry as a whole. Data sharing amongst institutions is also important especially for the war against emerging trends. Data relaying amongst industry players is also aiding attack prediction and monitoring. Big data supports conduct authentication that helps in the war against fraud. Some of the aspects the institution can detect in a bid to enhance security include the geographical location frequently used by the customer, a device used to access the account, and the speed of typing their security credentials such as passwords (PricewaterhouseCoopers 2014).

With the herein outlined importance of big data, this report will critically outline how banking institutions can capture this data and have safely without losing it. The numbers, through analytics, will provide an insight into business and product development bearing in mind customer satisfaction and security (PricewaterhouseCoopers 2014, p.4). The report will also deeply analyse how the mentioned data can be utilized in business continuity.

Data Collection and Storage

Digital technologies are setting a new configuration to the world. Smart devices that are always connected enabling real time connections at any given time and place are now a must have especially among the millennials. These trends have pushed for the demand of similar conveniences even from the banking sector and other financial service providers (Everett 2015 p14). In order to be in a position to gather such emerging trends that are shaping the consumption of financial services, banks have to rely on big data which has to be captured from multiple sources. Banks are now actively engaging in data collection about customers, the products and services they offer, their suppliers, and employees. Due to the dynamic nature of the data, the institutions have to gather this data on a real time basis at any point of engagement (Everett 2015 p.15).

Data Collection Systems

For a long period, banks have been moulded by massive statistics. Figures have been getting bigger, and the growth will persist for as long as banks exist. The values exist simply because banks have gathered momentum recently in the effort to collect it. Not all data is helpful to a bank, and therefore management has to come up with distinct categories of data that are crucial in fraud detection and mitigation as well as in business development (Everett 2015, p.15). Below are some of the types of data a bank should consider collecting.

Customer Data

Customer’s data is very important, especially in the loans and deposits business. It is, therefore, crucial for the bank to gather basic customer data (Somal 2017, p.2). The data is important as it helps to draw critical decisions that are necessary for long term credits and customer relationship management. Remarkably, the data collected is not only vital for profitability, but also in the evaluation future credit risk as well as anti-money laundering risk assessment (Somal 2017, p.2). The following data pieces should be considered by banks for collection.

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Email Addresses

Email addresses are important for communication purposes as well as marketing. Email addresses, however, go beyond the usual communication (Somal 2017, p.3). They can be used by banks to gather other varied pieces of information that can be sourced from public databases. An email address can be used to collect information through online searches.

Physical Address

Banks have made a stride in the segment by digitizing the accurate location of the customer (Somal 2017, p.3). This data helps banks in geo-mapping, understanding market concentration and also marketing. The facts are also used to assess the proximity of branch location to customers and compare that to profitability. Most importantly, banks rely on geography as a tool for setting up alerts for hazardous areas concerning credit perils and fraud such as money laundering, con activity, corruption, and cyber crime (Somal 2017, p.4). The data is used as a basis for the development of risk and scam management practices in conjunction with the regulatory bodies.

Financial Information

Such information is probably the most important set of data a bank may need to gather about any customer. A bank may want to know the economic activities a customer engages themselves in with the intention of detecting any fraudulent activities, especially money laundering (Somal 2017, p.4). For corporate customers, a bank may need to identify changes and levels of revenue, the margins of operations, the net income, levels of inventory, and salary scales (Everett 2015, p.14). While there could be other data, the above-cited items have given the overall best rank of information.

Product and Services Data

It is important for a bank to have an accretion of the products and services they offer to their customers. It helps a bank keep track of profitability of the products and keep note of the various touch points it has with its customers (Everett 2015, p.14). For instance, JPMorgan Chase and Company analyzed about 12.4 billion credit and debit card operations, and this analysis revealed that there was a slump in the daily customer spending between 2014 and 2015 (Somal 2017, p.5). The output guided the organization concerning restructuring its strategies as well as its offerings. With the capacity to track the areas of contact with the customers, it is therefore easy for a bank to track down the touch points that are susceptible to fakery. A bank can identify a product or a service that has weaker security measures allowing for deceit, especially, money laundering.

Employee Data

Employees are the most important segment of a bank. They constantly interact with customers. However, employees can be used as a conduit to perpetrate illegal business, for instance, through corruption and conspiracy to cover up shams such as illegal money business. It is therefore important for a bank to keep a record of its employee data concerning their details, their addresses, their role in the bank, as well as their daily activity (Somal 2017, p.5). The organizational structure should allow for timely reporting and clear accounting structures.

Data Collection Methods

Registration

According to Dumbill (2013, p.1), a register is a collection of information a bank can use to store varied data about customers, employees, and service providers. It can be a source of accurate enumeration. The implementation of registers comes as a result of the need to provide accurate data about the size of any element in a bank to facilitate close monitoring of banking activities to ensure that banking regulations are complied with (Dumbill 2013, p.1). Despite the fact that implementation of registers is usually for other reasons other than data collection, they can play a significant role in the scheme and execution of statistical systems, so long as the data contained in them are dependable, appropriate, and have no gaps.

Reporting

Banks may not directly send out agents for data collection for management purposes. They can rely on reports developed by credible organizations that have conducted surveys in the areas of business operations of the bank (Dumbill 2013, p.1). A good example can be the report by the PricewaterhouseCoopers on the threats that players in the financial services industry face. Such reports provide accurate and dependable data to banks that guide their operations especially in the fight against fraud. Banks can get a clear view of the most prevalent forms of economic crimes within the industry and develop mitigation practices to curb their negative effects on their operations.

Electronic Data

Such data is generated by the online or electronic activities of different stakeholders in the banking business. For instance, electronic data from Automated Teller Machine (ATM) cards can be able to detect any form fraud done using cards (Dumbill 2013, p.2). The data from these electronic machines can tell whether the security measures taken by the bank to secure personal information is sufficient or not. Data from online banking and mobile banking can be used to track customer behaviour (Dumbill 2013, p.2). It can also provide accurate information on how these channels can be used to perpetrate illegal transactions. Electronic systems are also prone to robbery activities such as hacking. Data generated electronically can tell the capacity of such systems to secure crucial files from hacking activities.

Big Data Storage Systems

The building blocks of banks are statistics. The data amount keeps increasing day by day, and as per the industry experts, the legacy storage infrastructure is continuously getting inefficient as well as obsolete. Such a situation calls for banks and other financial institutions to rethink and shuffle their storage practices to better effective ways that guarantee an allowance for expansion, security, ease of access, as well cost effectiveness. According to some experts, the biggest challenge banks have concerning consolidation of data of different types is to place it in a single location (Zulkarnain and Anshari 2017, p. 109). The scalability needed should be in a position to handle this situation, and the traditional systems lack this capability, thus, calling for an entirely new infrastructure.

Banks have now recognized the emerging need for almost instant access to data in centralized storage architecture, and are now abandoning the traditional mechanisms of storage where files are kept in silos (Marr 2017, p.3). The cost of holding critical values is also likely to decline with the new mode of information accommodation. 

Storage Requirements

 Big data storage in a bank is anchored upon the ability to scale up with the growing amounts and lowered latency when required for analytics (Marr 2017, p.4). In order to achieve this result, organizations that handle extremely high amounts of data run the hyper scale computing setting. The action involves a massive server with the directly attached storage capacities. Redundancy can hit an entire storage unit and thus in case of an outage affecting any component, replacement is wholesale. Latency in such an environment requires analytic tools such as Hadpool and NoSQL that are fitted with PCIe flash memory within the servers (Marr 2017, p.5). This mode of storage does not allow for sharing. The disadvantage here is that it is currently a preserve of large web-based businesses (Marr 2017, p.7). It also depends on the ability of a business to take in massive hardware and maintenance.

However, hyper scale infrastructure is not the only way to go for enterprises such as banks. Smaller business can as well enjoy the benefits of analytics of digits. They can handle facts and do so quickly without necessarily having the same response times as the web based organizations. They can utilize the scale-out NAS storage systems (Ohlorst 2013, p.11). It is described as a file access form of shared storage that can scale out to meet the expanding storage need.

Banks can also utilize object storage. The system tackles the same problems scale-out NAS does (Ohlorst 2013, p.11). It widens with the widening number of files. It does so by assigning a unique code, indexing, and assigning a location to the file. However, object storage is not as mature as scale-out NAS.

Data in Action

It is the process of collecting data, analysing it to inform action (Ohlorst 2013, p. 13). Data collected and stored by a bank can now be analysed to define it. The information can guide business growth with reference to consumer-centric products, detecting con, and coming up with recommendation engines.

Consumer-centric Product Design

Commoditization of banking is driving banks into the usage of big data in an effort to edge out competition (Ohlorst 2013, p.13). Banks are increasingly adopting advanced systems for data science techniques to facilitate collection, processing and analysing it to help in the delivery of significant enhancement of all fields of retail banking thus making banks more customer-centric.

In order to achieve customer-centric banking, big data is promising banks an efficient way to deliver products that are more targeted and cost effective concerning their design and marketing. Customer-centric products should deliver security (Ohlorst 2013, p.14). Data analytics in the bank ought to show any points of weakness that serve as a conduit to any form of fraud activities such as password hacking and seal them off guaranteeing security.

Retaining huge facts is not directly proportional to the best result. On the other side, big data does not directly translate to customer centricity. According to Kirkpatrick (2013, p.3), 37% of bank customers worldwide postulate that banks do not address their needs and preferences satisfactorily. Smart and selective application of data science procedures promises banks a new opportunity. The business of banks is a business of data. They are in a position to access more data around a customer than any other business especially with the surge in the use of web and mobile based services (Kirkpatrick, 2013, p.3). With the detailed data on customer profiles, rich information on spending and their income, and the geographical locales where people go to spend their time, banks are in a good position to achieve customer centricity.

Customer centricity cannot be purely achieved through financial services. Banks will have to look outside this bracket and use data to come up with creative best practices (Kirkpatrick, 2013, p.3). For instance, a bank can use a customer’s profile data to personalize communication and also geo-location tools for notification timing. These practices will turn a bank from being a place where money is stored and become a trusted brand that is wanted and appraised by all.

Recommendation Systems

With the surge in fin-tech products as well as peer-to-peer lending, disruption in the traditional retail banking is looming. Consumers have a higher chance of trying to venture into alternative financial services such as fin-tech products whenever the legacy retail banking fails to meet their needs (Simpson 2016, p.17). The phenomenon of abandoning the traditional banking is more likely to hit the millennials who according to PricewaterhouseCoopers (2017, pp.1-3) will form about 25% of the labour force in a place like the United States by 2020 and 50% of the labour force worldwide. Studies also show that 83% of the millennial population support their lives using credit cards (PricewaterhouseCoopers 2017, pp.4-5). These figures are a clear indication that if this group moves away from the legacy banking, revenue and profits of banks is under threat.

However, financial entities can tap into the millennials market through the use of big data to run a recommendation system (Simpson 2015, p.18). A reference platform direct buyers to the next most captivating offer based on shopping history of other similar customers. Big data in conjunction with the science of data gives the foundation for the development of customer profiles (Simpson 2015, p.18). Recommendation engines are achieved through a collaborative methodology that is based on data science and content-based filtration. This achievement ensures that a customer is reached by the most relevant product bunch.

Bank customers apply simultaneously a number of banking channels, especially web-based and mobile platform to access information on banking as well as conduct transaction on a real time basis (Simpson 2015, p.18). This act facilitates an increase in the varied nature of data banks can collect from customers that can be used to drive up sales. Upon consolidation and analysis, the data will not only be in a position to accurately profile a customer, and the market segment but also comes up with developed models for appropriate product bundling that can be offered at any given time.

Business Continuity

This term refers to the ability of a business to continue its operations at an acceptable predefined capacity even when disruption strikes. It involves using big data to enhance resilience by identifying the most crucial products and services, the most urgent activities that underlay these products, developing counter blueprints that will expedite the continuation of business operations, and capacitate instantaneous recovery from whatever the disruption is.

Online Business Survival in case of a Power Outage

As this report has demonstrated, big data may be the basis for the survival of any business. Any situation that interferes with the way that data is collected or its storage may interfere with the smooth running of business leading to losses due to issues like loss of critical files (Ohlhorst 2013, p.17). For online businesses that are highly dependent on the internet, power outages can interfere with it hence delayed service delivery. However, there are ways businesses can survive an outage.

An online business can obtain a cloud backup and recovery plans. A cloud backup solution for recovery can help business store their data safely without loss even in the event of a power outage (Ohlhorst 2013, p.17). Cloud services keep up to-date files securely in the cloud and facilitate its retrieval at any time.

Online businesses may require backup batteries or universal power supplies (UPS) for their computers and MiFi (Ohlhorst 2013, p.18). A power outage can take place in a flash manner and have the capability of lasting for days. A backup battery will guarantee an online business smooth operations in the event of a power loss. In the event that the internet is lost, it is always important to have a MiFi device as an alternative source. Alternatively, an online business can utilize mobile technology by creating a mobile hotspot. Consequently, a business will remain connected to the internet using cellular networks (Ohlhorst 2013, p.18).

Online businesses can also use surge protectors. During storms, power voltages can go down, and this can destroy computers. The quick flashes may have the potential to corrupt crucial data in the storage devices, making it difficult to retrieve the work that was in progress before the power surge (Ohlhorst 2013, p.24). A power surge protector in conjunction with a cloud service will guarantee the safety of computers and files respectively.

Conclusion

In conclusion, big data is crucial to the operations of banks and other financial institutions. Banks cannot detect any form of fraud without using data. Big data can be analyzed to detect the products that are prone to fraud, to get map areas that can be termed to as risky concerning fraud activities such as money laundering, and employees who perpetrate deceit in conjunction with customers (PricewaterhouseCoopers 2014, pp.6-8). Big data provides a frontier for business development for banks. In the wake of fin-tech technologies, revenue and profit margins of banks could get affected if organizations do not use big data adequately to develop innovative products. This situation can be achieved through customer centricity. Big data can expedite the augmentation of targeted products that are exciting to customers in a bid to enhance the loyalty of the existing customers as well as acquiring new clients (Schdeva 2016, p.9).

Businesses do not work in an environment that is devoid of disasters that can interfere with their operations. Such disasters may include power outages. These acts can adversely affect online businesses (Ohlhorst 2013, p.25). In order to ensure smooth running, these businesses are bound to put into place recovery solutions such as power backups and internet backups to keep them running. They can also acquire cloud solutions to safeguard their data.

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Recommendations

Banks should heavily invest in big data capturing and storage. They may also invest in advance data science analytics to derive sense from the figures. The analytics should be done in a targeted manner to derive relevant trends in the lines of fraud detection and to come up with customer-centric products. The future of financial establishments is in information, and if banks do not use it to edge out competition from fin-tech technologies that offer almost instant banking solution, they may find themselves in difficult businesses environments (Fontichiaro, 2010, p.8). Big data infrastructure that is put in place should be dynamic enough to detect emerging issues that might adversely affect the banking business.        

References

Dumbill, E., 2013. Making sense of big data. Big Data, 1(1), pp.1-2.

Everett, C., 2015. Big data – the future of cyber-security or its latest threat? Computer Fraud & Security, 2015(9), pp.14-17.

Kirkpatrick, R., 2013. Big Data for Development. Big Data, 1(1), pp.3-4.

Marr, B., n.d.. Big data in practice. pp.3-12.

Ohlhorst, F., 2013. Big data analytics. Hoboken, NJ: Wiley, pp.1-35.

PricewaterhouseCoopers, 2014. Financial Services sector analysis of PwC’s 2014 Global Economic Crime Survey. Threats to the Financial Services sector. [online] London, pp.1-17. Available at: https://www.pwc.com/gx/en/financial-services/publications/assets/pwc-gecs-2014-threats-to-the-financial-services-sector.pdf [Accessed 25 Aug. 2017].

PricewaterhouseCoopers, 2017. Workforce of the future. The competing forces shaping 2030. [online] London: PWC, pp.1-15. Available at: http://www.pwc.com/gx/en/services/people-organisation/workforce-of-the-future/workforce-of-the-future-the-competing-forces-shaping-2030-pwc.pdf [Accessed 26 Aug. 2017].

Simpson, S., 2015. How to turn big data into profits & greater market share. Texas Banking, pp.17-19.

Somal, H., 2017. Big Data & Analytics: Tackling Business Challenges in Banking Industry. Business and Economics Journal, 08(02), pp.1-7.

Zulkarnain, N. and Anshari M., 2017. Big data: Concept, applications, & challenges. Information Management and Technology (ICIMTech), pp. 1-129. DOI: 10.1109/ICIMTech.2016.7930350