Artificial Intelligence in Finance and Investments
The adoption of AI in the financial sector is driven by demand factors such as financial regulation demands, competition with other firms and profitability needs, and supply factors such as the availability of financial sector infrastructure and data and technological developments.
Artificial intelligence is in the process of making a number of breakthroughs in the financial industry, including the creation of applications that could disrupt the industry. Therefore, there is an assumption that AI can not only partially or completely replace human capital, but also improve performance beyond the human threshold.
In assessing improved performance, it is crucial to consider the implications of this development for financial stability. This article provides a discussion of the application of AI in financial services, particularly on the improvement of insurance services, algorithmic trading, fraud and money laundering detection, text mining, credit scoring, and the potential implications of each of these applications.
This application incorporates the use of proprietary algorithms to include information about price levels and changing market conditions, and to execute fast, automated trades. The speed at which trades are executed has led to their reference as high-frequency trading (HFT). Basic HFT strategies include order flow, mew releases and other high-frequency signals, high-frequency average value trading, and direction trading that acts as a formal and informal market token. Financial institutions, especially proprietary trading houses and hedge funds, have used this technology for the past ten years.
The benefits of algorithmic trading include faster trading at the best possible prices for the benefit of customers and the firm, by reducing the number of errors for emotional and psychological reasons, simultaneously improving the automated survey in multiple markets, and improving accuracy (less errors). they are incapable of processing information in a short time, which makes the algorithmic system the best option. Chan et al (2017) note that algorithmic trading must operate under a high level of regulatory scrutiny as it can lead to sudden crashes, similar to those experienced during and after the global financial crisis.
Usage Areas and Potential Applications in Insurance
The insurance industry has caused a delay in the adoption and use of digital technologies such as AI. Many financial professionals suggest that AI could phase out the need for life insurance agents and brokers. Forbes has observed that the ability to acquire and develop life insurance portfolios, monitor policies, and facilitate insurance can be managed by a robo-life-agent. Most insurers rely on automation of key system interactions and empowerment of insurance agents with robotic automation processes. This automation is the penultimate step on the journey to the full implementation of AI.
AI in the insurance industry is in the form of third-tier technology, such as the elimination of 34 employees and the adoption of the Watson Explorer Program (IBM) by Fukoku Mutual Life Insurance. It also offers first-tier augmented intelligence such as the Allstate Business Insurance Expert chatbot by Allstate (Chan et al.2017). Many insurers are also exploring Robotic Process Automation (RPA) systems that implement software in the workflow and processing of high-volume business processes. RpAs can be trained to communicate with other systems, trigger responses, manipulate data, manage operations automatically, and learn specific procedures. As an example of the implementation of RPA, in 2016 Davies Group had four employees process 3,000 claim reports every day (Chan et al. 2017). An estimate of the task without RPA shows that the team needs twelve people, proving how AI systems fill traditional human roles.
Credit Evaluation/Credit Scoring Applications
Credit scoring tools using machine learning seek to increase the speed of scrutiny of decisions while controlling for increased risk. For a long time, lenders depend on credit scores to arrive at credit decisions for retail customers and firms. Traditionally, most credit scoring systems have a payment history provided by financial institutions. and was based on transactional data. However, additional, semi-structured and unstructured data sources, including text messaging and cell phone usage activity and social media usage, have been transitioned by banks and other credit institutions to create a more accurate and comprehensive assessment of the credit balance sheet for loan seekers.
For example, it can evaluate factors such as use of big data for scoring, timely payment of utility and phone bills, and non-credit bill payments. The inclusion of machine learning algorithms in this repository has improved the evaluation process to capture qualitative factors such as willingness to pay and consumption behavior. Machine learning enables lenders to leverage more data that improves faster loan decision and cheaper, faster and higher borrower quality classification. However, the implementation of such data analysis processes leads to data protection and privacy issues. Another critical Artificial Intelligence tool in the evaluation of credit scores is the use of artificial neural networks (ANN). ANN is more accurate in bankruptcy forecasting and credit evaluation, which makes it an important tool in making credit decisions.
Machine learning algorithms used in credit evaluation and scoring have significantly improved access to credit. Traditional scoring models required large amounts of historical credit data to be subject to the scoring process of potential borrowers. When such data is unavailable, scoring is impossible and potential credit backup borrowers lack a chance to build a credit history or get a loan. Thus, the application of machine learning algorithms and alternative data sources will enable lenders to process credit decisions that were previously impossible. While this is extremely beneficial in reducing financial exclusion, we observe that it can lead to unsustainable increases in bad debt, especially in countries with deep credit markets. Research shows that the last few years have been marked by the emergence of a number of FinTech lending firms targeting customers excluded by the traditional credit system.
Another point of concern in using credit scoring machine learning algorithms is the “black box” aspect of customers denying transparency over credit decisions. The use of these algorithms makes it difficult for lenders to provide explanations to supervisors, auditors, and consumers about decisions regarding credit scores and subsequent decisions. The “black box” also raises concerns about the possibility of bias in credit decisions using unconventional data and online trends. Consumer advocacy groups claim that these algorithms can yield combinations that indicate the customer's gender or race, which could influence the decision. Such decisions flout fair lending laws. Chan proposes an AI evaluation model that solves the "black box" problem. They identify the main concerns of review boards, including the type of deviation found in the model and the model's dependence on specific outputs. Other factors include how the model handles unequal or missing data, processes for monitoring and ensuring the consistency of the output, and metadata type is included to back up the output. Finally, the board should examine the ability of the training set to process the confidence intervals of the model's output and the complexity of the data.
Another use of Finansa AI is in text mining, syntax (semantic) analysis, and news. AI is a useful tool for automatically reading and analyzing text such as news, reports, and social media content or activity. According to Zavadskaya, this application will transform investment services as artificial broadcasting machines will read all relevant news and information in seconds. This task initially takes people a few hours and still does not cover all the necessary information that can affect stock performance. It is vital in the analysis of trends. It can also assist in the prediction of institutional and regulatory changes and simulate the outcome. In trading, the ANN is a better predictor than the traditional linear regression model because stock markets are chaotic and dynamic.
Leveraging the use of this technology, a company scans through posts on social media platforms such as Twitter to identify popular news items and forward actionable alerts to subscribed investors before the news is officially released by a data miner that distributes an algorithm. Another company that practices data mining is AlphaSense, a search engine aimed at financial professionals. The company uses linguistic search algorithms to help individuals find the desired information in less time than other search engines. The search process uses natural language processing (NLP) to identify the most relevant information. includes its use. An important feature of AlphaSense algorithms is that they are self-learning, which means they learn from their past mistakes and improve the next search to increase efficiency.
Also, a company known as Kensho uses data mining to provide consulting services. Kensho uses nlp systems that can read submitted questions and review appropriate information to provide quick answers or recommendations. The algorithm can recognize correlations between stock prices and certain events and suggest investment decisions that people can easily miss in their analysis. The firm's clients are investment firms and individual investors. Kensho's business model is a threat to financial advisors who work with large banks, requiring large amounts of compensation and long deadlines to provide advice. Kensho and similar companies fulfill the tasks that these consultants can do in 40 hours, in just a few minutes, removing unwanted obligations such as sick days, bonuses, salary and maternity/paternity leaves.
AI fraud/anomaly detection is a vital tool. Pattern recognition helps identify behaviors that differ from standard patterns. For example, it can be important in identifying fraudulent insurance claims, fraudulent use of cash and credit cards, illegal financial practices and transactions, security threats, money laundering, transfer fraud, and illegal transactions. Mastercard uses this through its Decision Intelligence service, which was introduced in 2016. The service detects the use of artificial intelligence to detect fraud, specifically the use of algorithms to determine normal and abnormal purchase locations, customer shopping patterns, typical price range and shopping time for customers. These algorithms allow for rapid identification of strange behavior and blocking of accounts until clients provide explanation.
The banking industry could benefit from the deployment of AI tools in money laundering (AML) and anti-fraud detection management. Banks have long relied on a rules-based approach to control their enforcement, money laundering, and fraud risks. This approach is also known as risk scoring or a risk-based approach. Companies use this model to conduct a formal money laundering risk assessment and identify the risks they face depending on their business lines, products, and customers. After the assessment and risk documentation process is completed, companies submit their potential risk scenarios to regulators for approval after the start of aml transactions. The risks and uses of AI are well explained in AI in Software Engineering.
This traditional method is expensive and can generate false negatives or positives. Chan proposes that banks can use AI to run two parallel approaches: one that deploys AI tools in pattern recognition and the other that is based on an intelligence-based model that uses big data. These models are derived from transactions and customer personal and business details, where each bank uses its own dataset. As this poses data privacy issues, banks can integrate a distributed ledger system known as Blockchain that anonymizes transaction-level information. In this way, banks remain competitive and protect their customers, while controlling money laundering and fraud in the industry. contributes to their efforts.
As a result, the financial sector is experiencing a gradual evolution due to automation technologies and AI influence. AI adoption in financial systems is increasing at an exponential rate, causing a wide range of positive and negative effects. This article provides a discussion of AI-implemented tasks in the financial industry and the implications it causes. With AI constantly evolving at a rapid pace, firms and professionals in the financial industry must adapt to changes to enable them to take advantage of emerging tools with increased convenience.