By Alex Muchiri, an Alibaba Cloud MVP. He is the founder of Itesyl Technologies, a financial data and business banking solutions company.
The financial industry especially the banking segment is changing at an unprecedented pace with the emergence of mobile, artificial intelligence, IoT, Big Data, and other modern technologies. In this respect, industry players are contending with new types of risks, regulation black areas, and lack of understanding of the scope of their products. There are new players with specialist services such as online banking only, card only, or treasury services providers. On this note, the good old branch still offers valuable services to clients but are struggling to keep up in the digital space due to tough competition from cheaper, more technologically sophisticated online-only operators. So, what role does Big Data play in the modern finance industry?
The vast majority of players, especially banks, use Big Data (about 70% in the mid of the decade) to create a competitive advantage for their organizations. As banks continue to digitize their processes, they also need to adopt technologies such as Hadoop and Machine Learning to analyze massive data generated from their products and operations. As customers continue to rely more on digital banking, Big Data is contributing significantly. Some of the major contributions are:
Adopting Big Data analytics helps companies build risk models and propose methods to mitigate them. Trapped inside the data are potential risks that may increase the cost of doing business and financial institutions can uncover such risks through analysis of market trends, social media sentiment analysis, and spending patterns.
On the same note, banks can uncover and prevent fraudulent activities using machine learning algorithms that leverage massive amounts of data, which they already have. There are some patterns that identify bad creditors and such could be identified from their social media. While the process could take a painfully long amount of time if it had to be done manually, the use of Big Data reduces the time and resources required to make a conclusive analysis. In that context, Big Data helps financial service providers mitigate risks before they reach catastrophic levels.
Consumer analytics entails the analysis of spending patterns, investments, shopping trends, personal philosophies, and other backgrounds that are relevant to their finances. This helps in targeting customers with useful solutions and is a powerful lead generation strategy.
Financial service providers (FSPs) can track consumer activity and always know when they could sell them services. Big Data analytics uncovers patterns such as demographic patterns, behaviors of income level groups, and behavioral changes. It can also help uncover risky spending patterns to help customers avoid potential pitfalls. In the end, what we get is a symbiotic relationship between FSPs and their customers.
Big Data tremendously influences company policy and strategy. This is an emerging trend being experienced across the financial industry. Leveraging the improved forecasting using Big Data tools, companies can improve their predictions and incorporate them into the overall strategy. Less than ten years ago, companies relied on intuition and expertise to forecast without leveraging any data. However, financial planners and analysts today make data-driven forecasting based on indicators extracted from data. Big Data quickly diagnoses what is happening, why it is happening, and how it will impact on tomorrow's business, giving a company more room to navigate.
Digital transformation is one of the most significant changes in the financial industry and is largely powered by data. By unlocking new insights from analysis, companies better understand their products, usage patterns, and missing links. They can embark on closing those links with new digital products or services to retain their existing customers. For instance, machine learning algorithms can track transactions and realize that customers are paying for a particular service using their bank. The bank could move to make the process seamless by updating its mobile banking application or incorporating the new service and sell to their customers.
Another important influence of Big Data on modern finance is the improvement of operational efficiency. For instance, using Big Data, analysis of all compliance requirements from the government for banks can be automated and scaled to all customers. That way, banks can have an easier time filing compliance with the relevant agencies. Big Data also boosts overall performance in the industry by using performance analytics to determine optimal performance and make data-driven assessments. By keeping track of monthly, quarterly and annual targets, and developing market conditions using Big Data, decisions can be made to either scale up or scale down work efficiently.
Big Data is unlocking new opportunities by leveraging massive financial data and using advanced analytics. New opportunities for growth can be identified to launch new products, sell insights to other companies, and increase profits.
Overall, Big Data is playing a major role in finance today. It is much easier to extract customer sentiment from feedback on a massive scale. It is also helping companies identify and understand key value drivers for more predictable planning and customer satisfaction. However, the future of the industry also depends on how well FSPs continue to leverage Big Data in the coming years. Specifically, here are some trends likely to rely on Big Data in the future.
Artificial intelligence is rising quickly as an industry standard and is changing the way customers interact with FSPs. According to a study by Accenture, 75% of banks believe that AI will be the cornerstone of a client's engagement in the near future. It already transformed many industries including transport through driverless cars, natural language processing, autonomous mobility, machine to machine communication, among others. All this would not be possible without massive data used for learning processes. Some of the key trends expected in the industry include:
Presently, the application of analytics in finance is still quite generalized. However, the next generation of analytics will offer cutting edge insights for a smaller domain targeting specific industries. This will mark the rise of highly personalized and multichannel banking. Such capability will include GIS data, business data, movement, process, and more. Analytics in finance will advance enough to provide industry-reliable insights and products for e-commerce, manufacturing, process management, learning solutions among others.
The concept of predictive analytics is relatively young but as it matures, specialized analytics will become a norm and reliable enough to make accurate predictions based on Big Data analytics. At this stage, much of the analytics processes will be automated using machine learning and sophisticated AI. Competition in finance will soon depend on how prepared a company is to take up such advanced tools and use them to serve their customers.
New technologies call for new expertise and industry requirements. It is likely that in the near future, new careers in predictive analytics and product analytics will emerge with experts able to use Big Data for future predictions and make product recommendations. It is possible that data science professionals will specialize in domains such as business experts, consumer tech, manufacturing, etc.
Financial institutions serve customers with diverse requirements and behavior. Customer segmentation will transform the bank from a general-purpose institution to the one that places its customers in groups and targets them as such. Already, Big Data is helping institutions analyze customer behavior and spending patterns. In the future, the dynamic segmentation of customers will advance even further to include specialized products backed with AI.
As deep learning and AI become more operationalized and sophisticated, we will see the industry head into new directions from the current trends. AI-only banking apps might be one of such changes. It could also mean ditching the UI that we are accustomed to for a simpler, intuitive, and flexible interface powered by bots to deliver personalized feel unique to each user.
According to a 2016 study by Accenture, three are three models that will dominate the banking industry in the digital future. They include:
This new age of Big Data in finance is defined by openness, collaboration, and investment. Banks and other financial service providers must embrace external products and intellectual property to discover new opportunities and reach new markets. Such partnerships will advance even further to include specialized technologies in the field of data science in ways that will be most beneficial to customers. Obviously, investments will play a key role in the development of these new technologies and markets. Those companies that will not evolve and adapt to this new wave will be left out or rendered obsolete. It is prudent to start seeking expert solutions from technology providers such as Alibaba Cloud for a differentiated level of service.
Alibaba Cloud's Financial Services thoroughly incorporates the financial technologies and service capabilities accumulated by Ant Financial and Alibaba over the past ten years. It leverages artificial intelligence technologies and deep learning, allowing it to learn from every single financial scenario. With a wide range of functions, such as smart customer service, smart credit, and smart risk control, Alibaba Cloud can help organizations and individuals alike make accurate decisions. Such solutions are already optimized for integration into financial processes without massive financial investments that would otherwise be required to build and train models from scratch.
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