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.
The digital sphere is raining technologies. The influence of artificial intelligence is taking center stage with every possible improvement. Technology is changing almost all industries including banking and finance, healthcare, automobile, telecommunication, manufacturing, defense and military, entertainment and media, education, etc. The sub-domains of Artificial Intelligence such as machine learning, natural language processing, data analytics, and image analytics are also rolling out profitable use cases in diverse sectors. Besides, artificial intelligence is serving the business purpose by leveraging end-to-end automation processes. Therefore, Analytics Insight has listed the top 50 business use cases of artificial intelligence in diverse sectors. Predictive analytics is a gift to healthcare. Sometimes, we come across patients who say they underwent an unnecessary surgery due to a lack of predictions on what was coming. Fortunately, artificial intelligence is changing the fate of such burdensome risks and avoidable surgeries.
Swedish philosopher Nick Bostrom, in the book Superintelligence said, "Machine learning is the last invention that humanity will ever need to make." From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century. AI has made its presence felt in all walks of life due to its ability to help the user innovate. It has also enabled users to make faster and more informed decisions with an increased amount of efficiency. Of late, the banking sector is becoming an active adapter of artificial intelligence--exploring and implementing this technology in new ways.
AI is changing every industry and business function, which results in increased interest in AI, its subdomains, and related fields such as machine learning and data science as seen below. However, we also note that since COVID-19 outbreak, interest in AI, as measured by Google queries about AI, has been stable / possibly declining. This may be due to increased interest in COVID-19 and its effects during this period. However, this depends on the specific industry and applications, we see increased interest in AI in manufacturing during the same period. As of 2018, 37% of organizations were looking to define their AI strategies. There has been significant progress since then and according to a recent O'Reilly survey, 85% of organizations are using AI. To integrate AI into your own business, you need to identify how AI can serve your business, possible use cases of AI in your business. Marketing can be summarized as reaching the customer with the right offer, the right message, at the right time, through the right channel, while continually learning. To achieve success, companies can leverage AI-powered tools to get familiar with their customers better, create more compelling content, and perform personalized marketing campaigns. AI can provide accurate insights and suggest smart marketing solutions that would directly reflect on profits with customer data. Marketing analytics: AI systems learn from, analyze, and measure marketing efforts. These solutions track media activity and provide insights into PR efforts to highlight what is driving engagement, traffic, and revenue. As a result, companies can provide better and more accurate marketing services to their customers. Besides PR efforts, AI-powered marketing analytics can lead companies to identify their customer groups more accurately.