Implementation challenges and the role of AI to unlock the potential of Big Data in advanced financial services

The world now produces 2.5 quintillion bytes of data every day, meaning that approximately 90% of all data currently online was created in the last two years. Big data and its analysis are already revolutionizing many different sectors of business, with financial services among the most heavily affected.

Half of the world’s adult population already uses digital banking and experts estimate that the amount of banking data being generated will grow 700% by 2020. The institutions that can quickly process and make sense of this deluge of data will gain a massive competitive advantage over those that fail to adapt and therefore allow themselves to be overwhelmed.

Making proper use of big data will allow financial institutions to become more efficient, more responsive to customers, and ultimately more profitable. Of course, humans will never be able to process this enormous amount of information on their own. The use of Artificial Intelligence will be crucial to unlocking the full potential hidden within the growing mountain of big data.

While artificial general intelligence – an AI that can reliably pass the Turing test and convince observers of its humanity – remains elusive for the time being, we have seen tremendous advances in the narrow AI designed for specific tasks. Machines have now far outstripped our own ability to play games like chess and Go, and are performing better than humans across many other tasks as well.

When analyzing data, AI can now think and decide more like a human than ever before, with progressive learning algorithms that allow it to improve upon its capabilities over time. Today’s AI already goes far beyond humans in its ability to process big data using quantitative precision, text analysis with contextualized understanding, and automated image recognition.

Moreover, AI can now give solid goal and risk advice to clients, as well as an overview of the current market situation, simply by compiling and analyzing data from public sources. Banks can now deliver advice at scale for a mere 40% of pre-AI costs.

The value of any tool depends on how it is used. For this reason, businesses embracing AI must first be clear about their big data goals. Many companies that attempt to use big data are ultimately unsuccessful because they do not have a long-term focus.

Clarity in the planning stage allows for open consideration of the challenges related to new systems being put into place, along with the likely impact of those challenges. It also allows the organization to weigh the potential benefits of new opportunities, in order to determine whether the effort is likely to bring an overall advantage both qualitatively and quantitatively.

Key success factors should be decided at the outset, so that the organization can check its progress toward goals during the process of implementation. By forming a comprehensive strategy that makes full use of AI’s capabilities, the business will be better prepared to integrate its data processing efforts into its overall plan for success.

AI can now analyze vast amounts of real-time data to help with dynamic investment strategies and trade execution. Such a powerful data processing tool is ideally suited to this type of work since it can make large calculations extremely quickly. AI can even observe patterns in old data and use them to predict whether those patterns are likely to repeat. With data from tumultuous financial times such as the 2008 crisis available to be analyzed, AI can play an invaluable role in planning for future anomalies of a similar type.

AI can even make individual portfolio suggestions for clients based on their level of risk aversion – advising them on when to buy, hold, or sell depending on the agreed-upon strategy.

Several prominent financial services providers in Thailand are already using AI for key functions. Since 2017, AEC Securities in Thailand has used the AIPRO, an AI service which executes trading strategy using in-depth data analysis. AIPRO also adjusts its recommended strategies based on shifting market conditions and historical trading patterns.

Kasikorn Bank uses AI for SME credit analysis and micro-loan underwriting. The software quickly analyzes client behavior to determine creditworthiness. It then allows accepted clients to obtain loans of up to THB 1 million, with the entire process taking only one minute via mobile phone. The bank estimated it would give out loans in excess of THB 3 billion through the platform last year.

Many Thai banks are already using chatbots – most notably, SCB’s Connect, Krungsri’s Bella, and TMB’s Me. Chatbots can respond to customer inquiries and complaints through content-based and contextual interpretation of clients’ text messages. The bots are faster and more efficient than any person at answering simple questions, freeing up employees for more important tasks.

The ability of AI to detect patterns can be used in other ways as well. For example, AI also opens the door to quicker, more accurate fraud detection and prevention. It can track past spending behaviors to pinpoint abnormal account activity – such as using a card in another country too soon after use in the home country, or attempting to withdraw unusually large sums of money. These types of AI capabilities will also improve with use. Once a human corrects an erroneous fraud detection, the system will continue to learn and make increasingly sophisticated judgements.

With better-than-human image recognition, and an ability to simultaneously sync with various databases, AI offers an excellent way to verify identity and perform background checks more accurately than ever before.

AI is already instrumental in unlocking the potential of big data, and its influence will grow ever stronger in the future. Financial services organizations must adapt to this digital disruption or risk falling far behind their competitors.

The process of implementing AI will create drastic change across any organization. It is therefore of crucial importance that AI be implemented carefully, and with a clear plan for the future.

Some financial institutions hesitate to make a full commitment to this new technology, and decide to adopt it in a piecemeal manner for individual projects. Yet the very power of AI rests in its ability to integrate different types of information from a variety of sources. An organization-wide commitment to the adoption of AI is essential to reap the full benefits of big data analysis.

Far from replacing humans, the best use of AI in financial services involves working in conjunction with trained personnel. AI is well suited to large-scale repetitive jobs, which frees up human capital to deal with the more subtle and interesting aspects of financial services, thereby adding value even to the parts of the business that AI does not directly touch.

Like all the best tools, AI should be understood as a resource that empowers those who know how to use it. Although foreign and perhaps mysterious at first, it is quickly revolutionizing the financial world in much the same way as the invention of the calculator. As with other disruptive technologies, the businesses that adopt it intelligently will pull far, far ahead of their more traditional counterparts. In an industry as competitive as financial services– or any other data-rich environment – embracing the future is indeed the only way to stay viable.