Within the next ten years, Cognitive Computing will be omnipresent – not only from a customer‘s perspective but also from that of the companies. Most companies will have cognitive systems which provide findings, advice and recommendations for internal as well as external decision support and perform actions similar to those of customers or companies. For financial service providers in particular, systems and applications with cognitive abilities may well be the only way to efficiently use today’s enterprise data architectures. This will sweep away numerous current business models (“disruption”). So how would financial service providers best react to this development, and what does the future of the industry look like?
Presently, financial service providers are facing dramatical changes on many fronts: Customer expectations have increased, and so have the requirements set forth by regulatory authorities. What is more: the floods of daily business information has grown to tsunami-like proportions. This pressure has caused them to pay a lot of money for the use of enterprise data and processes to develop new business models and proposals. In the area of infrastructure, they work on consolidating structured and unstructured data in secure environments and thus gaining new abilities. However, the platforms and applications that use these abilities intelligently have not yet matured fully. Until they have, the increased pressure for profit will ensure that banks will hang back when it comes to fill vacancies. Apart from that, the employees required – mainly data scientists and analysts – would be difficult to find anyway at the moment. This is exactly why the existing personnel now need support via intelligent and autonomous systems that “pre-digest” data, understand them, draw conclusions and use the data as a basis to assist in making decisions - cognitive solutions.
Food for AI: Data, Data and even more Data
For more than 30 years, financial service providers have continuously been dealing with automation and innovation. Back then, Mainframe used to be the prevalent platform. But today it is all about Big Data, social networks, mobile apps and Cloud Computing, with cognitive systems functioning as a catalyst for innovation. These are being used in risk management, wealth management and financial consulting, for analysis of credit risks, processing of insurance claims, managing Customer Churn and identifying frauds among many other fields. The competition in the financial sector is growing, especially when it comes to offering customers innovative and personalised services based on individual personal profiles.
The new generation of customers has a strong affinity for technology that clearly surpasses that of previous generations. They demand more transparency in their relationship with the financial service provider and want to be sure that they receive the best service as well as additional value. At the same time, the range and the depth of the available offers makes financial decisions considerably more difficult. So, the customers need assistance and support, a point that will also significantly drive the demand for cognitive solutions that offer help with research, decision making and support customers in achieving their financial goals. If these systems understand individual requirements and orders, they will recognise chances, risks, and reasons that the users have not foreseen but nevertheless have a substantial impact.
Artificial Intelligence (AI) and cognitive systems are the foundation of automated and personalised services such as recommendation machines, automated consultants and customer-specific messages. AI techniques like machine learning with neural networks use an enormous amount of structured and unstructured data for Deep Learning through Natural Language Processing (NLP) and analysis. Cognitive systems understand questions and instructions, they can formulate hypotheses, and provide potential answers on the basis of available findings and insights. By gathering huge amounts of data, these systems are trained to adjust previous answers and self-learn. As a result, the user interface and experience for these cognitive systems becomes immensely important, be it natural language processing, language recognition or the inclusion of social feeds.
Cognitive solutions as the standard
Cognitive Computing brings about economic, operational and technical advantages and thus reinforces digital strategies in the financial sector. Venture capital companies invest billions of dollars in automaton methods and areas that were previously reserved for knowledge workers such as business and security analysts, sales experts, managers, doctors, lawyers, bankers etc. A common aspect of all these professions is that they spend a lot of time analysing and checking data. Numerous companies offer tools and solutions that can multiply and expand the abilities of an organisation’s employees. To this end, the best employees’ knowledge, experience and decision-making competence are aggregated and converted into software. For such cognitive solutions, leading analysts expect a growth of more than 30 percent per year over the next five years. They will ultimately replace other software classes and become a part of the common computing infrastructure in the next decade.
An essential factor for the emergence of cognitive solutions is the pressure on the knowledge workers who are of tremendous importance for all organisations due to the change towards knowledge-based professions. These employees are under a lot of stress because they must keep up with the information flow for their daily decisions. Knowledge work – i.e. knowing when, how and why a specific action happens – is a key component for most of the companies in the financial sector and can heavily influence a company’s profit. According to IDC, a typical organisation with 1000 knowledge workers wastes approximately 5.7 million dollars every year because information gets searched for but is not found. 61 percent of the knowledge workers regularly access four or more systems to get the information required, 15 percent even eleven or more. Over a third of the working time (36 percent) is spent searching for data on multiple systems and consolidating it.
The potential is gigantic – and the entry barrier into the technology is surmountable for every financial enterprise thanks to state-of-the-art platforms for hyper automation now available. The intelligent use of AI and cognitive systems will very soon make the difference for the end user in banking and insurance industry in the near future. It is impossible to implement the customers’ requirements concerning personalised and individualised financial services without these technologies. So put that data to use!
