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Artificial Intelligence for Financial Decision Making

Dec 04, 2018 | 2 years ago | Read Time: 5 minutes | By Amrita Chakraborty

Artificial Intelligence for Financial Decision Making – The Evolution for the Future

 Artificial Intelligence (AI), by far the most promising research domain for scientists, is the technique to imbibe human intelligence in a machine. Machines are efficiently trained so that they perform and carry out a host of human-like operations. Nowadays, AI has widespread applications for problem solving in various sectors, be it technology & robotics, academics, industries, defence, healthcare or financial and banking domains. In this context, to enable AI create value, a lot of algorithms like neural networks, decision trees, random forests and swarm intelligence is gradually gaining prominence in research labs for the financial industries.


What is artificial neural network (ANN)?

Artificial Neural Network (ANN), a popular AI based tool has immense potential in financial problem handling. ANN or Neural Nets are computer programs which simulate the problem situation in hand by an efficient training algorithm. It iteratively learns through experience, and can continue and modify its learning process in conjunction with a changing problem environment. The ANN is a well- established and proven technique to deal with prediction, clustering and classification of new data. These aspects find profound usage in the finance sector, where potential applications include assessment of bankruptcy risk, identification of arbitrage opportunities and technical and fundamental analysis (Hawley, 1990). Certain financial problems are discussed which are efficiently resolved employing the ANN tool of AI. In recent times, variation of the ANN has become popular as deep learning and convoluted neural networks have started gaining prominence.


How does AI work in a financial simulation?

The financial management setup of all businesses comprises of an immensely complicated and dynamic situation. All complex financial operations are broken down into simple multiple subtasks where the interrelations between those subtasks are even more critical. ANN handles such situations with efficiency in a corporate financial environment. ANN is used to model these subtasks such that they are trained to operate as static or dynamic entities in accordance with changes in financial organization of the company. Real-time examples include prediction of financial behavior of a firm’s customer. Here, the input parameters consist of economic and customer specific data whereas the output parameter would be expected purchase or payment behavior of the customer. On analyzing previous history of customers, the ANN is trained. This is useful to develop a system to perform bad debt expenses analysis, cyclical expansion and contraction of accounts received, cash management, evaluation of capital investment, asset and personnel risk management and prediction of credit cost and availability based on the data of company’s financial performance.

How does AI work in the banking sector?

Artificial Intelligence is employed to carry out jobs like account maintenance, fund verification and background record checking in a better and quicker manner involving less expense as compared to human intervention. Banks of USA have developed an innovative AI based tool, Einstein, which allows customers to access banking services simultaneously from their personal devices, desktops and as well as in person. JPMorgan Chase’s COiN, or Contract Intelligence platform, employs the concept of AI to deal with commercial loan agreement contracts. This step is effective enough to reduce 360,000 hours of annual work load for lawyers and loan officials. In April 2017, Wells Fargo Bank devised an AI-based chatbot which communicates with customers to provide account information and password resetting service via Facebook Messenger.

In near future, AI is believed to support financial sectors in maximizing resources, minimizing risk factors, and be more profitable in trading, investing, banking, lending and in fintech domains. Artificial Intelligence provides a guiding platform for banks and financial companies to save time and money by the implementation of algorithms to gain a better insight and prediction regarding company’s sales performance and ensure improved customer service delivery.


How does AI work in stock trading?

AI helps in implementing stock trading rules and decisions by processing data based on algorithms (Chakraborty, 2016). Corporate firms have implemented such AI based algorithms trained by data based on human emotions and behavior, extracted from social media analysis (Rathore, 2017) and statistical data collection. In Hong Kong, stock trading is conducted autonomously using AI based algorithms whereas Nomura Securities in Japan depends on AI based robotic traders for enhanced profits in share transactions.


How AI helps in investment?

In investment management domain, AI is used in analyzing the investor’s portfolio, risk tolerance and tracking of previous investment history to provide guidance regarding choice of future better investment prospects. Live updates (24×7) on global stock market trends and personalized investment advice are also available using AI. The objective of such usage is to maximize returns on investment while minimizing risks of investments across projects, which often for a variety of investment portfolios becomes a highly complex optimization problem.

How AI works in loan sanction and money lending?

AI plays a vital role in money lending, reducing regulatory costs, credit scoring and lending (Chhonker, 2017) and in credit risk assessment. Loan sanction processes implement AI for credit lending applications to assure efficient, quicker and accurate risk assessment, to ensure better customer feedback. AI also helps creditors in collecting the outstanding loans and generates insights which are often difficult for human workers to ascertain.

How AI helps in financial fraud detection?

AI based algorithms (Chakraborty, 2017), analyze various data points to pin point and identify every possible fraudulent activity and financial mal-practices which human officials often tend to overlook by mistake. This highly improvises real-time accuracy and helps in reducing false declines. Employing AI techniques to track unusual and faulty records and improvise the general work pace contributes to the growth of financial organizations to be more efficient and accurate in their processes.


All in all, the growth of AI is increasingly being felt in the domains of financial management. As we move from the predictive and prescriptive analytics era to the space of cognitive analytics, the role of these AI algorithms like neural networks, decision trees, random forests, genetic algorithms, swarm intelligence, and others would gradually become more and more prominent with the era of automation with smart technologies driven by sensors (Kar 2016; Chakraborty, 2017; Gupta et al, 2018). In the days to come, the way these technologies drive value for the firm, is something where a lot of research efforts are being driven continuously. Experts in data science are continuously focusing on promising applications which would change the way AI makes interesting insights from the huge volumes of data getting generated in the financial services industries.

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