Artificial Intelligence has caught the attention of the world, including financial institutions. While AI-based FinTech solutions may not get the same attention than autonomous cars or robot dogs, its impact will undoubtedly be felt. AI-based FinTech solutions will both save financial institutions billions in cost and create billions in additional revenues, potentially creating more than a trillion in additional profits in the financial services industry. A study done by Accenture showed that the implementation of AI in the financial sector could lead to a 31% increase in profitability rates by 2035. Moreover, AI will allow to customize financial services delivered to clients, leading to an enhanced customer experience.
Compared to other industries, the finance industry jumped quickly to finding value with artificial intelligence. Currently, AI is being used in a variety of ways within the financial services industry. The most prominent use case for AI is in fraud and anomaly detection. When fraud occurs, financial firms end up covering fraud prevention services for the impacted victims. In addition to having to manage funds lost through fraud, financial institutions often find themselves tangled in a variety of other issues pertaining to the loss of reputation.
They may have no choice, if they wish to survive. Consumers, accustomed to experiences with Amazon, Netflix, and Starbucks, demand rapid fulfillment of requests, personalized solutions, and constant attention from their financial providers. With the wealth of data possessed by banks and credit unions, consumers not surprisingly expect providers to know them, value them, and reward them for their relationships. Given the rise of digital and challenger banks, traditional banks and credit unions must find new ways to maintain their share-of-wallet and customer trust. Technologies that integrate artificial intelligence and big data analytics provide financial institutions with unprecedented visibility into their customers' financial dynamics, enabling the kind of personalized service which they crave.
This research work deals with Natural Language Processing (NLP) and extraction of essential information in an explicit form. The most common among the information management strategies is Document Retrieval (DR) and Information Filtering. DR systems may work as combine harvesters, which bring back useful material from the vast fields of raw material. With large amount of potentially useful information in hand, an Information Extraction (IE) system can then transform the raw material by refining and reducing it to a germ of original text. A Document Retrieval system collects the relevant documents carrying the required information, from the repository of texts. An IE system then transforms them into information that is more readily digested and analyzed. It isolates relevant text fragments, extracts relevant information from the fragments, and then arranges together the targeted information in a coherent framework. The thesis presents a new approach for Word Sense Disambiguation using thesaurus. The illustrative examples supports the effectiveness of this approach for speedy and effective disambiguation. A Document Retrieval method, based on Fuzzy Logic has been described and its application is illustrated. A question-answering system describes the operation of information extraction from the retrieved text documents. The process of information extraction for answering a query is considerably simplified by using a Structured Description Language (SDL) which is based on cardinals of queries in the form of who, what, when, where and why. The thesis concludes with the presentation of a novel strategy based on Dempster-Shafer theory of evidential reasoning, for document retrieval and information extraction. This strategy permits relaxation of many limitations, which are inherent in Bayesian probabilistic approach.