Artificial Intelligence for Banking Sector
Automation technologies have shifted their role from being merely an enabler into the disruptors of traditional ways of business processes. We have been witnessing huge investments in automation technologies across all the sectors in the industry for a long time now. One of the most talked about automation technologies that have been impacting the adoption level of technologies in the industry is Artificial Intelligence. A blend of machine learning, natural language processing and cognitive learning, AI is helping the organizations to increase efficiency and scalability.
If we talk about sector-wise investment, the Banking sector has clearly emerged as a front-runner among the first adopters of AI. According to one of the recent reports, Banking industry is going to be second in terms of AI/cognitive spending this year with around $3.3 billion investment. With cut-throat competition and high dependency on manpower-led processes, for the banking sector, AI has emerged as the knight in the shining armour. AI is helping Banking and financial industry to increase workforce productivity, efficiently cutting down the redundant tasks and thus helping in making operations more hassle-free and impactful.
AI-based chat-bots are the most common use case in Banking and Financial Services providers. These chatbots are simulating human chats without any human intervention and collecting a massive amount of data for the behaviour and habits of the user and learning the behaviour of the user which helps them to adapts to the needs and moods of the end user. Chat-bots have already started to revolutionize the customer relationship management in the banking industry.
Although chatbots seem to be the primary use case of AI for the banking industry it is not the only use case. There are other key areas of AI applications that can revolutionize the industry in the coming years. Here are few of the areas:
Fraud Analysis and Investigation:
The banking industry has been one of the top operating industries in world wide web susceptible to fraudulent users. As technological infrastructure grows more complex, so do the demands on those protecting companies and people from fraud. This is where AI technologies have been increasingly positioning themselves as a key technology to help automate instant fraud decisions, maximise the detection performance as well as streamlining alert volumes in the near future. For e.g. Citi Bank invested in Feedzai’s next-generation machine learning platform that helps in recognizing fraud threats and at the same alerting customers in real-time to protect against fraud. Although AI adoption for fraud analysis and investigation may still be in its infancy, new adoptions have gained significant momentum and showed positive results.
Personalized Financial Services:
Providing a singular, seamless and synchronous experience to each customer has become a vital business operative for banking and financial services industry. But with increasing number of customers and complex back-office operations, it has become clear to the banking industry that they have to move away beyond traditional transactional relationships and embrace a new standard of providing highly personalised and integrated customer services. Among all the automation technologies AI has emerged as the leading tool to take the charge on turning traditional banking, as it shifts the heavy reliance on face-to-face interactions, to one on digitally led customer service. This helps the organizations to increase efficiencies, cut costs, and forces leadership teams to use a customer-centric approach at all times, increasing brand loyalty in the long-term.
Risk management is of the highest priority for banking and financial institutions due to its dire consequences and ripple effects in the ecosystem. AI-powered risk management for banking and financial institutions involves fraud detection, Anti-money laundering solution, customer assessment and stress testing. AI solutions gain intelligence from various data sources such as credit score, financial data and spending patterns to identify a risk score of a customer. The ability of machine learning to analyse a large amount of data with more granularity is improving the analytical capabilities in risk management and compliance and helping analysts to make more informed decisions.
Insurance underwriters have to analyse and evaluate the potential risks involved in the process of ensuring applicants and their assets based on the information provided in the application by the applicant. However, there is usually no assurance that the information provided is accurate. Machine learning integrated with natural language understanding (NLU), insurance underwriters will have access to more sources of information that will reveal more information about a potential client allowing for a more effective and efficient risk assessment. By utilising AI that automates the underwriting process, insurers are able to translate to a better pricing for insurable risk which will prove itself beneficial in the long run to both the insurers and clients.
AI is here to stay and is already impacting almost all the industries, the banking sector is an early adopter of this trend. AI is a bundle of endless possibilities and its potential to benefit the banking sector cannot be overstated. This is high time for banking companies to start investing in AI in case they are not already doing it if they hope to stay in business with their competitors in near future.