Artificial Intelligence in Financial Industry
Artificial Intelligence in Financial Industry
Artificial intelligences are a collection of new and existing solutions that have are highly upgraded that are disrupting the financial industry and monetizing the data of the new opened opportunities. Artificial intelligence software’s involved in financial industry have the capacity to make human-like decisions more efficiently and faster without making errors. Fueled and trained data on artificial intelligence is able to unlock emerging opportunities on the economic and commercial sector because it is able to efficiently introduce insight in the core system.
Credit Risk Management through Artificial Intelligence
Credit risk will guarantee the bank that lend funds to the borrowers through the artificial intelligence tools. Recent financial changes that are hugely impacted by artificial intelligence emphasizing more stress on credit risk management system to make banking operations efficient, for the need of diversifying the risks (Konovalova et al., 2016). Credit risk management system will help the bank to know the credit risks from their borrowers who may not timely make their payments. The process of credit risk management will help to better manage the credit portfolio by using the tools and techniques of artificial intelligence. It will identify the potential risks, risk treatment, measuring risk and evaluation of the actual risk models through advanced computerized tools. The primary goal of credit risk management in a bank will stabilize finances of the bank because it provides clear definition of granting credit policy. Practices of credit risk management play an essential role in determining the liquidity, profitability and soundness of operation of the bank. Artificial intelligence tools and techniques play a pivotal role in provision of effective and efficient practices of credit risk management help bank to design a framework and a system at a corporate level of attaining prescribed risk exposure limit. The purposeful understanding of credit risks management is the primary component of credit process that credit risk management framework has, which is multidimensional focusing of the long-term survival and profitability. Risks associated with lending are critical especially when the counter party fail to meet the agreed terms obligation, resulting to elevation of credit risk in banks especially if the loans were given to the borrowers without getting enough knowledge about the borrower or the capacity of reimbursing the loan. Banks should give careful attention to potential impacts of credit risk levels on the profitability of the bank, because extreme risks put serious threats to the bank which places it at the edge of closing down the operation of the bank. Credit risks directly impacts the strength of financial and bank earnings. Various practices should follow to ensure that the risks are managed by implementing several parameters the observe the intensity of risk such as healthy risk management practices to accomplish the goal of the government.
Identification, measuring, monitoring and controlling risks exposure are crucial steps of credit risk management. Its framework consists of rules and policies that ensures that it maintains the balance between returns and risks that allow the diversification and quality assets of the bank. Therefore, credit risk management help the bank in bringing the dangers that it may encounter in an acceptable parameter or take reconciliation step between the profits and level of risk to prevent the bank to undergo risks that has negative impacts of the bank earnings. All bank’s activities are placed through granting credits and raising funds, that is why they are essential for long survival and running success.
Initiating credit risk management framework in the use of artificial intelligence involves several approaches that ensures the bank’s aspects concerning credit risks limits are determined and assessed. Its components consist of; policy frameworks ensure that the bank’s operation is supported and a clear detailed guide for the ongoing maintenance of an organization structure. The policy framework is crucial because the goals and procedures are clearly set out, which are essential for decision making and negotiating of guide of setting more policies through the help of the tools of artificial intelligence (Belás et al., 2017). The tools of artificial intelligence are used to benchmarking tool for monitoring and evaluating progress in policies that support the bank. The framework as provides tools for rating the credit risks of the bank. They include credit risk analysis, models and scoring to ensure the credit risk management is effective and efficient. The credit risk limit helps the banks to identify the risk exposure they may counter from the industry and with other peer industry in the same region. Merton, KMV and Altman’s Z score are the model are crucial tool for identifying the potential credit risk exposure. The credit risk management mitigation in a bank is crucial because they are able to credit derivatives, guarantees and collateral securities that prevents the occurrence of the potential credit risks in banks. Credit audits are crucial in banks because the able to review the quality of credits and administration of credits. The audits aid in measurement of the quality of credit portfolio and reporting of compliance of regulations. The bank is able to review the review loans that identify the potential problems areas through use of artificial intelligence and prompt loans that show the adequacy of loan loss provision, and ensuring that there is proper documentation of loans. credit risks management in bank is essential to strengthen the bank’s system.
Clear establishment of the organization structure through artificial intelligence is critical for the Credit risk management system is presented in different units in a bank organization. Considering the different units of the organization, the bank should allocate responsibility effectively by ensuring that the employees have the performance capability of the responsibility assigned to do. For a bank to succeed and accomplish the set-out goals, they should set out priorities such as empowering the employees to perform their roles for long run success and survival (Belás et al., 2018). The bank should clearly communicate the responsibilities through the emails, chat box, WhatsApp groups and text message to its employees. Implementation of the artificial intelligence in the system of the bank organization is crucial, for it requires technical expertise that inform specialized skills, communication system, verify risk models. The bank should assign individuals accountable for bank’s operations, by appointing an independent risk management committee who carry out the task of assigning tasks. The committee is crucial because it is able to design, policies, framework and strategies that help mitigate the credit risks in the bank.
Portfolio in Hedge Funds and Pension Funds
Hedge funds and pension funds are becoming favorite to private and institutional investors in the last few years. For instance, ABP and CalPERS have been ranked as the largest pension funders in the world whereby they announced their biggest plans of investing several billions of dollars the asset class (Broeders et al., 2019). The decision of involving head funds in allocation of funds is comparison of portfolio without or with funds. The components of hedge funds are well represented by hedge fund index that are available in the public. The strategy is considered heterogeneity between indices whose primary objective is to reflect the same strategy like those of different indices that cover different parts of hedge fund universe. It is essential to understand that some indices of hedge fund may contain more bias on survivorship than others, which may result to different perceptions of investors of the hedge fund performance and heavily added value that depends of studied index. Another problem occurs when investors do not invest in hedge fund index that is vital for analysis. A bank may have a larger number of funds, meaning that it requires use through a fund of fund structure because the calculated index may have a much larger number of funds. The bank should ensure that it does not present a much larger number of funds than the people it may contain because the index may not be a good proxy for its employees. Such misinterpretation may result to serious consequences in the investment process outcomes because the investors may expect one thing but end up getting something different.
Models of construction of hedge funds and public pension fund allocation portfolio are crucial to maximize the wealth utility of taxpayers and compensation. I would construct portfolios in hedge funds and pension funds ranging from 1-30. I would calculate six different samples of statistics of the monthly returns of the portfolio. Begin measuring the location of the location of return distribution, then the overall dispersion of the possible outcome, then skewness of the return distribution, kurtosis of the extreme outcome possibilities, 500 S&P correlation and bond index. It gives insightful understanding of the significance of hedge fund diversification. Ahn & Wiersema (2019) argue that it is expected that elevation of number of funds, the standard deviation of the portfolio substantially drops the return. Correlation and kurtosis go up, while skewness drops with the stock market (Ahn & Wiersema, 2019). Therefore, adding more funds results to relatively large rises probability of losses, while the potential of diversification with the context drops larger stock-bond portfolio. Changes occurs mostly of relatively small portfolios, so the average population index will be fair for the average of more funds. To construct the portfolio the data is obtained from TASS past record of the funds containing the monthly net return fee of the hedge fund and pension fund of a total of 2238 from June 2016 to May 2019. Due to ambiguous data and incompleteness 180 funds were eliminated. I also eliminated pension fund form the sample. By May 2019, 1800 funds were left alive and 634 of funds were dead. I created 475 samples of funds to replace the funds that were closed down for the period of 7 months. I assume that just in case of closure of funds of the investors it is possible to role funds into end month report of net asset values and at zero additional cost. From the 475 funds that were created from different 500 portfolio of equal size N, N=1, 2…,30, by random sampling without replacing. Most probably the bank investors would not select portfolios by random sampling. I would select the funds based on their statistical properties to ensure that there is track of records of the investment records, although such information may not be sketchy at the best. Assuming that all funds weigh equally, we calculate the monthly returns on every portfolio. The portfolio sizes will range from 1 to 30, the 5th, 10th, percentile that result to frequency distributions. The horizontal line of the graph will show the value of the population for the portfolio that equally weighed in all 475 funds (Hooke et al., 2019).
Potential Risks of Using AI in Financial Industry
Financial industry should consider the risks that come along with tools of artificial intelligence, so they are able to regulate them from a place directly or through cooperation with suppliers who manage risks (Treleaven & Batrinca, 2017). The primary objective will ensure that they and their customers are benefiting from technology use. Financial industry requires huge amount of money to maintain and produce artificial intelligence as they are very complex machines. They are expensive especially when the financial industry needs to advance the software programs, especially if occur regularly to meet the changing environment. In any case that the tools of artificial intelligence critically fail, process of recovering codes lost and reinstating the system require large amount of money and time to take care of it. The financial industry should that the appropriate expertise engaged are effective with provision of artificial intelligence.it vital to understand all that because the transitioning services that occurs in the artificial intelligence may end up creating internal gap of skills. These gaps may widen if the individual to understand the what happens to their data, that could result to loss of control of the data, adequate resources of monitoring, reviewing and decision that are explained by the artificial intelligence. Artificial intelligence helps in replacement of workforce using the machine which may cause huge impacts of the financial industry when it comes to reaching the unemployed people widely (Truby et al., 2020). The employees of the financial industry become rampant because they highly depend of the machine, which results to losing their creative innovation power. Not only to banking, artificial intelligence may increase the rate of unemployment, resulting to devastation on the mind use of individuals with nothing to do. Critically review the skillset of personnel is vital to ensure there is an appropriate team if individuals with correct level of experience and skills wo can manage and review the relationships of the providers of the services of artificial intelligence. Internal session for training employees and employers is crucial to ensure that everyone in the financial industry is equipped with updated information
The artificial intelligence learns and improve in its efficiency to perforce task in the financial industry, but it is not able to make judgement. Therefore, the humans take the role of individual situation and calls for judgement into account when making decisions, that artificial intelligence is not able to do. If there is replacement of adaptive behavior of humans the artificial intelligence may cause unreasoning behavior within things and human ecosystem. The finance industry may experience challenges of using the artificial intelligence because their systems are complex. The hackers may try using the opportunity to access the data of the bank and inserting the wrong data into the system. It is known as data poisoning whose attempts are concerned with impacting the outputs of decisions made by the system for the sake of benefiting the detriments of their own companies. The financial industry is at risk of offer a lot of power to individuals controlling it. It means that it takes control away and the poses high risks as well dehumanizing actions in numerous ways. It is risky if the artificial intelligence is placed in the hands of the wrong people because it may a serious threat thanking. That means that if individual controlling the artificial intelligence think destructively, they may end up generate havoc with advanced machines. Financial industries experience potential risks in protection of data because collection and use of data across the world is unregulated. The non-compliance law of protection of data run risks of scrutiny, fines and damage of reputation (Matilda Bez & Chesbrough, 2020). The company are having difficulties when processing compliance with data protection law that help the company provide services that reach high value measuring of the organization data.
In conclusion, use of rapidly evolving technology by the consumer and commercial borrowing is vital because the artificial intelligence make more accurate and faster assessments of borrower potential, accounts for wider variety of factors and at a lesser cost. Artificial intelligence tools are effective in detection of frauds because it helps the financial industry to build models that best in prediction. It enhances the decision making around transactions of fraudulent cards, detection of mortgage fraud and money landers. Artificial intelligence is crucial for provision of short-term prices in the market by forecasting the financial market. It plays a vital role in dealing with enquiries in the front office of the customer.
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