Financial firms were the early adopters of the mainframe computer, relational databases, and have eagerly awaited the next level of computational power. Inorganics Intelligence helps Fintech companies in solving human problems, by increasing efficiency. Artificial Intelligence (AI) improves results by applying methods derived from aspects of Human Intelligence at a beyond human scale. he computational arms race of last 2 decades has revolutionized the FinTech companies. Technologies like Machine Learning, Artificial Intelligence (AI), Neural Networks, Big Data Analytics, evolutionary algorithms, and much more have allowed computers to crunch huge varied, diverse and deep datasets than ever before.
In early ages of Banking, bankers used to have personal connections to their customers to help them assist well for their decisions. But in this digital world, this personal connection has lost. Can technology bring back the human connection? Artificial Intelligence (AI) at many levels can be leveraged to bring back that connection. Artificial Intelligence and Machine Learning can process the huge amount of information about customers. This data and information are compared and results in suitable services/products that customers want. This essentially means finding what’s right for your customers and hence can achieve customer satisfaction at the high level.
Data-driven management decisions at lower cost lead to a different style of management, where insurance leaders and future banking agents will ask the right questions to machines, rather than to human experts. Machines will then analyze the data and will come up with the recommended results, which can help leaders and their subordinates take better decision.
Automated Customer Support
Customers facing systems such as text chats, voice systems or Chatbots can deliver human-like customer service or expert advice experience at a low cost.
Fraud detections and Claims Management
Analytics tools collect evidence and analyse data necessary for conviction. Artificial Intelligence tools then learn and monitor user’s behavioural patterns to identify rarity and warning signs of fraud attempts and incidences. Claims management can be build up using Machine Learning (ML) techniques in different stages of the claim handling mechanism. By leveraging Artificial Intelligence and handling a huge amount of data in a small period of time, insurers can automate handling mechanism. It can even fasten certain claims, to reduce the overall processing time and also the handling costs while enhancing customer experience. These algorithms identify patterns in the data to help recognize fraudulent claims in the process. With their self-learning abilities, Artificial Intelligence (AI) systems can then adapt to new undiscovered cases and further enhance the detection over time.
Insurance management with AI systems will automate the underwriting process and utilize more crude information to make better decisions for the customers. Automated agents can assist the user online, in determining insurance requirements. Insurance usually comes into the picture after the loss has occurred. Automatic underwriting can extremely speed up the process and often deliver expensive tests unnecessary by linking several relevant data sets, even external ones that are not present in the medical records. Instead of paying for the treatments that are costly for insurance it’s better to detect the risks and diseases to prevent them. One can hence employ the data that was used before to access the risks, to then lower the probability of damages happening to the insured and also for the insurer.
Automated Virtual Financial Assistants
Automated financial assistants and planners assist users in making financial decisions. These include monitoring events, stock and bond price trends according to the user’s financial goals and personal portfolio, which can help in making recommendations regarding bonds and stocks to buy or sell. These systems often called “Robo-Advisors” and are increasingly being offered both by established Financial companies and Fintech Startups.
Predictive analysis in Financial Services
Predictive analytics in financial services can directly affect overall business strategy, sales nurturing, revenue generation and resource optimization. It can serve as a game changer by enhancing business operations, improving internal processes, and surpassing competitors. Analytics works closely with organizations across a broad range of industries to gather and arrange the data, analyze it using our leading edge algorithms and technology and briskly deploy customized, prescriptive solutions unique for each customer. Predictive analysis can help calculate credit scores and help prevent bad loans.
Predictive analytics uses a massive amount of data to find patterns and predict insights. These results and insights can reveal what will happen next: what the customers are going to buy, how long your employee might last, etc. Predictive analytics include everything from sophisticated statistics to Data mining.
Wealth Management for Masses
Digital and wealth management advisory services offered to lower net worth market segments, resulting in lower fee-based commissions. Smart wallets developed using artificial intelligence monitor and learn user’s behaviour and actions. These instruct users to restrain and alter their personal finance spending for saving their expenses.
Because of the significant potential benefits, there will be a high increase of automation in Financial Industry, often employing Artificial Intelligence. No longer just the objects of fascination in science fiction, Artificial Intelligence, Machine Learning and bots in Finance have potential to expand skills, reduce costs and improve the customer experience. This requires Fintech industry to work closely with the coders, developers, designers and tech people to ensure new concepts are diagnosed, developed and commercialized effectively and professionally.