The introduction of artificial intelligence in the banking sector makes banks efficient, helpful and more understanding. The growing impact of AI in this sector reduces operational costs, improves customer support and process automation. AI-based applications help banks by reducing costs thereby increasing productivity. Also, intelligent algorithms are able to spot inconsistency and fraudulent information in a matter of seconds. According to reports, nearly 80 percent of banks are aware of the potential benefits that AI presents to their sector.
There's no doubt that the finance industry is undergoing a transformational change. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The finance sector, specifically, has seen a steep rise in the use cases of machine learning applications to advance better outcomes for both consumers and businesses. Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Right from speeding up the underwriting process, portfolio composition and optimization, model validation, Robo-advising, market impact analysis, to offering alternative credit reporting methods, the different use cases of AI and Machine Learning In Finance are having a significant impact on this sector.
AI can be used in banks to decrease financial risk, It can improve loan underwriting through machine learning, improve financial crime risk with advanced fraud detection, It can improve compliance and controls, and reduce operational risk through improved accuracy in transcription & production of documents, banks can use machine learning and big data to prevent criminal activities and monitor potential threats to customers in commerce. Artificial intelligence (AI) includes machine learning and natural language, it can be used in the banking industry, Machine learning is a method of data analysis which automates analytical model building, Machine learning occurs when computers change their parameters/algorithms on exposure to new data without humans having to reprogram them. Natural language processing (NLP) refers to the ability of technology to use human communication, naturally spoken or written, as an input that prompts computer activity, natural language generation (NLG) refers to the ability for technology to produce human quality prose, It sorts through large amounts of available data to produce a human-sounding response, NLG can take the form of speech, or of a multipage report summarizing financial results. AI can help the bank understand the expenditure pattern of the customer, The bank can come up with a customized investment plan & assist the customers for budgeting, banks can send the notification about the advice for keeping a check on the expenses and investments based on the data, The transactional & other data sources can be tracked to help understand the customer's behavior and preferences to improve their experience. Artificial intelligent can sift through massive amounts of data and identify patterns that might elude human observers, One area where this capacity is particularly relevant is in fraud prevention, Artificial intelligence and machine learning solutions are deployed by many financial service providers to detect fraud in real time.
Conversational AI is a type of artificial intelligence that facilitates the human like conversation between a human and a software system in real time. It is a piece of software that a person can talk to, like chatbot, social messaging app, interactive agent, or smart device. These applications enable users to ask questions, get opinions, find support, or complete tasks remotely. Conversational systems are powered by a conversational engine named NLP (Natural Language Processing, a branch of AI that deals with linguistic and conversational cognitive science). They make use of large volumes of data processed with machine learning, and natural language processing to aid imitate human interactions, recognizing speech and text inputs and translating their meanings in different languages. Businesses can setup automated chatbots or virtual assistants that can communicate with humans via voice or text and in different languages of user preferences.
There is a fresh wave of disruption post COVID-19. Banks and financial institutions are adapting digital transformation at a blazing pace, this is a good and a progressive sign for us as this opens doors to the much awaited advancements in the financial sector. Lets just say – now the digital revolution has truly begun. The COVID-19 crisis has triggered customers to adopt digital interaction across segments. Nowadays branch loving customers are also using digital platforms to interact with their banks or NBFCs (Non-Banking Financial Companies) – this routine may become a trend in the future.