How artificial intelligence has disrupted the retail and financial services industry

Technology has fundamentally changed the manner in which business, commerce, and networking are conducted. Innovations related to data analytics and artificial intelligence have expanded the realm of possibilities for a range of industries from automobiles to energy. With these revolutions come a host of uncertainties as well. Forecasting the future has simultaneously become more predictable but more uncertain. Likewise, technology has become more secure but also much more dangerous. With each new iteration of technology, new advances in products, goods and services emerge. This has ultimately ushered in a new wave of prosperity not seen since the industrial revolution, where nearly all market participants are impacted. Through these innovations however, it is important for consumers to be cognizant of the impacts of artificial intelligence on information and security management (Li, ).

To begin, as noted in the introduction, technology has altered nearly every industry across the world. To better understand in the impact of artificial intelligence on information and security management, it is important to understand how technology is implemented in society. The pace of innovation around the world in unprecedented. With these technological changes, industry disruption occurs. A classic example, current underway is that of the financial services industry (Hu, 2018).

Traditionally, the financial services industry was relatively stable and entrenched industry in society. Banks often acted as intermediaries between investors and savers. As an intermediary, banks would facilitate the exchange of capital between entities through various forms and products. Historically, consumers would deposit funds in a bank. In exchange for the deposit, banks would pay an interest rate to the consumer who depositing funds into the bank. While funds where deposited, the bank would then lend a portion out, keeping a small portion as collateral. The funds that were lent would come in various forms including mortgage loans, auto loans, and other products. The difference between the interest rate banks paid to depositors and the interest rate they received from lending funds to consumers was known as net interest margin. So long as the bank operating prudently and had strong risk management policies, the bank would continue to make stable profits and grow. The ability for banks to act as financial intermediaries for most of the worlds most important transactions has continue for decades as this was, at the time, the only way to safely and securely transfer capital from those who don’t need capital (savers) to those that need capital (Borrowers). These functions where exacerbated during establishment of the capital markets. Here, investors looking to raise capital to expand a business could only do so by leveraging the expertise of an investment bank. Here, the investment bank would then go to the capital markets and raise funds in the form of an IPO. Much like the lending example above, the ability to raise capital was solely under the jurisdiction of financial intermediaries of banks. Overtime, as these functions grew so too did the concentration of market share with the banking industry. Here more commercial and investment banks began to take every larger portions of market share as consumers had little to no alternative. Here, a large physical branch network, nationwide ATMS, and a large teller staff where are needed by consumers. As a result, the largest banks where in the best position to offer these services (Zhu, 2015)

Now however, technology is severely disrupting this business model. Fintech firms have emerged, heavily utilizing artificial intelligence, big data, and data analytics to fundamentally change the manner in which banking is offered. Here, these businesses employ a variety of information management techniques design to help them make quicker and much more accurate lending decisions. This has led to a trend of financial disintermediation. This a very stark contrast to the industry prior to big data and artificial intelligence. These technological innovations have decoupled the natural monopoly that the financial industry has enjoyed for decades. The emergence of these Fintech disruptors has caused many firms, including financial businesses, to recalibrate the manner in which they leverage busines intelligence, artificial intelligence and big data to improve decision making capabilities.

Once such method used by incumbent banks was to leverage artificial intelligence to increase the virtualization and the use of paperless transactions on mobile devices. This has helped to improve customer relationships and the “stickiness” of products that use bid data solutions. Artificial intelligence and big data have also expanded the scope of product offerings particularly in areas such as China and India, which both rely heavily on mobile devices to transact only (Zhao, 2019).

In addition to innovative product development, higher customer retention, and seamless service, artificial intelligence also provides benefits to internal operation staffs as well. The integration of artificial intelligence technology and commercial banks will improve the management efficiency and cost of the banking industry. As noted above, financial firms traditional used a large amount of overhead to create their monopolistic positions. This overhead primarily consisted of a large network of bank branches along with the corresponding staff. In addition, overhead costs where incurred with large customer services staffs designed to be available 24 hours a day to tend to customers call. Likewise, an extensive network of tens of thousands of ATMs require maintenance to ensure they function when customers demanded. This overhead can be very costly which deterred other competitors from entering the market who could not offer a similar value proposition. With artificial intelligence and information management, systems can be more easily and seamless integrated. This allows firms to use their data in a manner that competitors cannot. This ultimately helps to improve the processes of pre-warning, in the moment processing, and post procession functions. Elements such as fraud detection and security concerns are greatly enhancing as systems are more seamless integrated through the use of information management and artificial intelligence. Even further artificial intelligence can automate routine task and eliminate the need for staff altogether, saving the company valuable overhead resources. Elements such as predicting customer questions, recognizing an unauthorized sign-in, and offering products that are likely to be purchase are all the result of innovations related to artificial intelligence. This combination of the ability to conduct remote customer service from anywhere in the world, the ability to enable predictive analytics, and the reduce the need for manual services, all improves corporate profitability. Fintech firms are not the only institutions using innovative information management practices, some of the world’s largest insurance companies are using artificial intelligence to help serve both the clients and stakeholders more effectively. Here insurance companies use standard AI techniques to help predict the possibility of auto accidents, the likelihood that an accident will occur in a give area, and which areas are more likely to have high insurance claims. Insurance companies leverage their large amounts of data to make better informed decisions through artificial intelligence (Chen, 2014)

Financial institutions are not the only entities impacted by artificial intelligence. The retail industry has fundamentally shifted the manner in which consumers interact with products along with expectations around service delivery. Much like the financial services industry described above, the retail industry was heavily entrenched within society for a few different reasons. Historically, retail firms flourished on information asymmetry. It was very difficult of consumers to compare prices as retail firms heavily utilized various mailers and catalogues to encourage shopping behavior. This catalogues where very cumbersome to use and where often full of thousands of products. As a result, consumers often relied on a handful of catalogues to assess the available value propositions in the market. Here, companies such as Sears, Macy’s and JC Penny dominated the market. Through economies of scale these firm where able to capture market share much in the same manner as financial institutions did in the above example. Retailers with economies of scale where often able to command more favorable purchasing terms from suppliers. Through favorable purchasing terms, these firms where able to charge lower prices to competitors thus creating strong barriers to entry. These firms through their nationwide store footprints where also able to create global brands known around the world. As these brands expanded consumers demanded them within their communities. As such, these retailers leverage their brands to occupy the most favorable positions within malls and shopping centers around the nation. Overtime, through this strategy, not only did the best retailers own the best land in the nation, they also had the best prices due to economies of scale. The larger store footprint also allowed them to carry a large assortment of products, which attracted still further customers and clients. This create a virtuous cycle that, much like the financial services example above, created a handful of dominate companies. Customers where often beholden to the price these retailers charged as there was not data analytics available to help consumers decipher what the appropriate price of the product was relative to other competitors in the market. As a result, more effort was required on the part of the consumer to research proper sales data and determine which retailer offered the best price

Today, the power dynamic has shifted to data analytics and information management solutions. Consumers now have the power, and retailers are now using data to help serve them better. Information asymmetry as it relates to pricing is no longer the hefty burden it once was. Consumers can seamlessly search and compare price, product specifications, and inventory levels in real-time. They can then leverage this information to make better informed and more rationale decisions, ultimately saving both time and money. Due to this shift in consumer power, many of these once dominate firms have disappeared. Both Sears and JC Penny have essentially disappeared as they both have entered bankruptcy. Macys, is just a fraction of what it once was, as it continues to shrink its store footprint and reduce headcount. The rapid ascent and descent these large, once dominate retailers, can be attributable to artificial intelligence and information management. For one, just like the financial services example above, artificial intelligence has allowed smaller and more nimble competitors to enter the market. These competitors do not have the large amount of overhead as the once dominate retailers once possessed. Ironically, some of the best retailers in the world do not own a store! Instead, through the use of data analytics to properly curate customers and deliver to them products, theses businesses are better able to capture market share (Lu, 2017).

A classic example of the use of data analytics, artificial intelligence, and information management is that of Amazon and Alibaba. Both compete in the e-commerce space albeit with slightly different operations and strategies. As it relates to artificial intelligence both heavily utilize automation of routine processes to help expedite operations. Alibaba heavily utilizes robots through many of warehouses to help pack, sort, and disseminate parcels throughout China. On Sept 28th, 2021, Alibaba announces that its driverless robots delivered 1 million package deliveries across China. According to Alibaba, 200 of the company’s robots have carried parcels to more than 200,000 consumers in 52 cities across 22 Chinese provinces. By 2024, Alibaba expects to deliver roughly 1 million packages a day throughout china. This is particularly possible as the company has invested billions of dollars into its autonomous driving segment which relies heavily on artificial intelligence (Tang, 2016). The company, its latest earnings press release stated that it currently uses advance algorithms to achieve a low-cost, mass deployment format for its self-driving robots. In fact, according to Wang Gang, head of autonomous driving at Alibaba, the company is able to produce and operate the robots at roughly one third the cost of the industry average. According to Alibaba, the robots use a cloud-based proprietary system with simulates over 10,000 possible scenarios such as extreme weather, wind, lighting, fog and other methods of poor visibility. The robot, on a typical day encounters roughly 40 million obstacles and can navigate its way past them 99.9% of the time due to artificial intelligence and data analytics. What’s more, the company plays on using these cloud-based algorithms with self-driving trucks to further exacerbate the trend of technology and logistics. Interesting, as of this writing, retailers around the world are grappling with strained supply chains that are underpinned by antiquated systems. Here, many supply chains are using systems of their predecessors that can not properly communicate and handle large changes in demand as a result of the COVID-19 pandemic. Many components on the supply chain do not properly communicate with each other in an effective manner thus creating massive bottlenecks, higher costs, and inefficiencies with the delivery of consumer products. Many of these tasks such unloading shipping containers, unboxing packages and so forth are still done manually, which is both cost inefficient and costly. As a Alibaba has demonstrated through its use of artificial intelligence, many of these processes in the future will be automated to help avoid many of the supply chain constraints and bottlenecks that are hurting consumers and businesses alike (Shenzhou, 2017).

These innovations are not limited to Alibaba’s self-driving robots, its core commerce division also leverages artificial intelligence and information management to provide seamless service. Much like its American rival Amazon, Alibaba leverages deep learning and natural language processing to help recommend products to shoppers. In addition, Alibaba then communicates to both its suppliers and retailers on projected demand to help them stay stocks with high sought-after products. Likewise, the companies Dian Xiaomi product is an artificial intelligence powered chatbot that can answer nearly 90% of customer queries. It services roughly 3.5 million user a day and has now even improved to understand a customer’s emotions during a chart to notify human customer service representatives.

To summarize the two above case studies related to the financial services and retail industry, artificial intelligence has heavily disrupted the manner in which businesses can engage with customers. As noted above information management is a critical component to unlocking many of the benefits derived from artificial intelligence. Using the above case studies, both industries, from a historically perspective relied heavily on physical overhead. This overhead including store branches, bank branches, tellers, sales associates, ATM’s and even building maintenance staff. Each of these elements, at the time, where required to achieves economies of scale and a market dominant position. As many of these items required heavy investment, it was difficult of competitors to enter the market and properly compete. However, as companies began to leverage information management techniques and artificial intelligence, many of these old benefits began to erode. Consumers no longer require bank branches as they now could transact through their mobile device. Fintech firms emerged using artificial intelligence to provide seamless loan approval in a manner of minutes. These firms allow customers to open checking accounts, savings accounts, and even transfer money all through proper data management. Many of the firms leveraging artificial intelligence didn’t have an bank branches and instead conducted business entirely online. This reduction in overhead costs allowed these businesses to be much more profitable. They where able to invest heavily in marketing to attract customers who where compelled by the use of technology to disrupt the industry. As a result, many of the older financial institutions where forced to adopt artificial intelligence, big data, data analytics into their own business operations.