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Banking on big data tools and techniques to unlock the benefits of unstructured data in Banking

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Banking on big data tools and techniques to unlock the benefits of unstructured data in Banking Assignment

Research Article 1 - Tomorrow's AI-Enabled Banking 

APA7 Reference

Jaiswal, A. K., & Akhilesh, K. B. (2019). Tomorrow's AI-Enabled Banking. Smart Technologies, 191-200. https://doi.org/10.1007/978-981-13-7139-4_15

Aims and Research Question

The complex essence of consumers' financial demands at various stages and the challenges confronting the conventional banking approaches has culminated in restructuring banks' business model. Automated judgments and behaviour powered by artificial intelligence allow banks to represent their clients in a systemically responsible manner. In consumer financing, AI usage has driven banks to recognize individual needs and states and tailored offerings to personalize their strategy.

Background Research

With the tough rivalry in finished technologies, business treasurers who own their company's partnership with banks must somehow be assured of rising their banking share. Only if there is a comfortable degree of openness, the pace at which transfers, and collections are performed from all around the world. The usage of AI can easily facilitate all this. Another bad experience is when businesses refuse to compensate for their data entry or human mistakes like erroneous IBANs, names of payers or number of accounts.

Methodology Employed

In Forbes journal, Kensho's co-founder Daniel Nadler quoted: "Advanced knowledge is a misnomer. It is about achieving stuff you traditionally believed a human being couldn't achieve at a dazzling clip. The next period be the "AI era" when AI has been the defining strategic advantage for businesses, public bodies and investor practitioners."

Tools and Techniques

The two steps above are done in seconds. The settlement funds transferred at the end of each working day, from cards given to the network of the association's network to the acquisitor bank and, thus, the account of the dealer, with the transaction fees and the merchant's rate of discount charged, would be billed to a bank. The issuer of cards also publishes the purchase on the monthly billing statement of the cardholder.

Outcome

To derive tangible advantages from AI developments, the rules on the use of banking consumer data and adherence with the risk aversion principle allow the banking sector a particular fix. There must be a severe risk and regulatory restrictions for the implementation of any modern methodology. However, this has prevented banks from embracing this as a probability across decades and then taking incremental, steady measures to introduce AI in any area of goods, services, and risk aversion.

Reflection

This paper provides a very exhaustive introduction to the domain which is very crucial for anyone who wants to begin further research in this very domain, hence it is very important for my research.

Research Article 2 - Mobile banking and AI-enabledmobile banking

APA7 Reference

Payne, E. M., Peltier, J. W., & Barger, V. A. (2018). Mobile banking and AI-enabled mobile banking: The differential effects of technological and non-technological factors on digital natives' perceptions and behavior. Journal of Research in Interactive Marketing.Chicago

Aims and Research Question

In the banking sector, rapid technical development in conventional Banking networks, including artificial intelligence, has played a transformative function. This research explores variables that affect mobile banking digitals' behaviours and expectations and the ease of AI-enabled mobile banking.

Background Research

The research would not involve age categories beyond digital aboriginal citizens. A study is constrained. Additional study is required to test differential effects across age ranges. The finding of no significant impact on AI's mobile banking benefit requires a further analysis of the correlations and disparities between mobile and AI-enabled banking.

Methodology Employed

Data from 218 digital indigenous communities has been obtained. In this article, the disparities between technology-based effects (i.e., attitudes towards AI, relative importance, perceived confidence and protection in real mobile banking activities) including non-technology (i.e., service need, the standard of service) variables relevant to the usage in mobile banking and mobile AI services are analysed in the form of multiple regressions and two different regression analyses.

Tools and Techniques

To draw digital natives, the banking industry prioritises access to financial accounts through mobile banking anywhere. This is essential for college-aged consumers who do not reside near their local financial institution. The article also indicates that increased one-on-one interpersonal communication with bank workers needs to be done with mobile banking functions.

Outcome

This study explores the reasons for mobile banking and mobile banking services that are allowed by AI. The findings demonstrate the disparity between our two dependent variables on how digital natives interpret a relative benefit. The comparative advantage construct, along with previous research, impacts more on the use of mobile banking. However, the close help of AI-enabled mobile banking was not necessary, showing that an additional layer of difficulty goes beyond comfortable, fast banking.

Reflection

This paper touches on the premise of combination of mobile technology and AI when it comes to banking and financial institutions. This is especially important considering the data that most of the users of technology these days use their mobile devices to perform financial transactions.

Research Article 3 -Artificial intelligence in banking (A lever for profitability with limited implementation to date)

APA7 Reference

Kaya, O., Schildbach, J., AG, D. B., & Schneider, S. (2019). Artificial intelligence in banking. Artificial intelligence.

Aims and Research Question

To date, IT solutions have concentrated in the business community primarily on automating repeat operations, which would otherwise entail human participation. Developers established limitations for these IT applications, and their capabilities were limited by designing these solutions. They were mostly static and unable to understand or act alone. However, this is changing more and more as technology evolves quickly.

Background Research

AI is being evaluated to detect fraud in online banking in real-time and to deter fraud. Indeed, in recent years, credit card fraud has been one of the most popular types of cybercrime, aggravated by the substantial rise in online and mobile payments. AI algorithms track the plausibility of credit cards purchases by consumers in real-time to detect fraudulent behaviour and equate recent transactions to previous sums and locations. When it sees threats, AI blocks transactions. AI is also checked to check the identification of consumers in KYC processes.

Methodology Employed

Artificial intelligence applies, without human interference and participation, to computer systems' capacity to acquire and implement information. AI systems draw assumptions and take reasonable measures by studying the environment around them and autonomously processing the details. They benefit from past assessments and develop their consistency over time, based on the degree of precision.

Tools and Techniques

AI algorithms can scan client records, and the authenticity of the details presented by an internet comparison is assessed. When AI algorithms find incompatibilities, a red flag is raised, and bank workers execute a more thorough KYC cheque. Chatbots are another field in which banks play with AI technology. Chatbots are automated assistants who communicate by text or speech with customers and aim to answer their questions without bank staff intervention.

Outcome

AI will revolutionize certain facets of our daily lives. AI has been drawing significant investments and is gradually being introduced, particularly in the US and China in recent years. In Europe, the image is quite misleading with a few countries with an involved AI landscape, others lagging. European politicians also adopted steps to improve AI operations in Europe, with their awareness of their ability. AI also has the power to alter facets of financial markets radically. However, banking rollout has been moderate to date. In the future, legislative measures concerning data security and the tightly regulated existence of banking may hinder AI's introduction. Nevertheless, AI need not neglect its future contribution to bank profitability. In a world where competition is significantly greater for banking, the fast introduction of AI innovations could be vital in keeping banks competitive - thanks to data-driven financial services service providers, including entrepreneurs in financial technology (FinTech) and big technology corporations that threaten historically banking business models.

Reflection

This paper is more oriented towards the fin-tech industry, which is the new generation business coming out of mixture of technology and finance. This is particularly important to understand the current issues and challenges in the implementation and trends in same.

Research Article 4 -Role of Artificial Intelligence in Combating Cyber Threats in Banking

APA7 Reference

Soni, V. D. (2019). Role of Artificial Intelligence in Combating Cyber Threats in Banking. International Engineering Journal For Research & Development4(1), 7-7.

Aims and Research Question

Hackers are exploiting various virtual environments to develop cybercrime with the developments of information technology. The bank and the banking sector try to leverage artificial intelligence to counter cybercrime and cyber-attacks. AI technologies, which enable the banking sector to fuel prosperity and development, offer many opportunities. To retain the trust in artificial intelligence, accountability and skill must be upheld. Artificial intelligence strategies include knowledge regarding consumer behaviour and interest. Robo-Consultation is an automated AI-controlled platform. Often involved in the security of personal data is artificial intelligence. AI's proper design for the financial industry, from which abuse in purchases may be detected. AI is linked to the computer protection realm directly. AI-based fraud prevention mechanisms deter and recognise different forms of cybercrimes. However, artificial intelligence is introduced and managed at high prices. AI strategies are also rising with this unemployment rate.

Background Research

Financials business will now allow extensive use of Artificial Intelligence strategies, becoming more familiar with digital consumers. Artificial intelligence methods retain the skill of machinery as well as human thought, logic and decision-making. Because of this aspect AI grows in money related administration, the bank enhances creativity and lowers costs for handling devices and knowledge storage. However, Nanette Byrnes claimed in the Technology Analysis that falsified design adoption, visual awareness, traditional distortion of dialects and theory era, etc., are considered regardless of rapid invention development. Credit scoring is not a recent application; it is easy to say that credit scoring is one of the first mathematical modelling methods in the finance business. On the other side, the banks attempt to obtain mathematical reports, decision-making bodies, regression and transactional data; these variables will sustain the customer's credit risk and have appropriate means of repaying the loan. Artificial intelligence techniques allow better access and reliable scoring. This approach is used to minimize risk and the amount of false and false positives. Banks with the observation of artificial intelligence, pick the most acceptable debit schedule. Banks may ensure credit risk support by artificial intelligence, a consideration that specifically influences financial stability. This is important since many monitoring criteria are in this area, including the European Banking Authority for Regulatory Implementation Professional Specifications for an Internal Rating Approach. This phase is essential. Comparability of model outputs and risk-weighted exposures is targeted according to targeted criteria.

Methodology Employed

Chatbots for customer support Map testing was successfully performed before chatbots were created. Their usage by managers of some portion of the account and numerous organizations of consumers is narrowly focused. It is also observed that chatbots are being shipped by Bank of America, American Express and MasterCard, allowing consumers to discover more sustainable applications, speed up consumer requests and give customers more cost-effective help. Furthermore, it is mentioned that this method rotates with customer service with the computerized logic. There are many illustrations both in money management and in the past; they are not entirely wrongly informative. Chatbots are selected for the trees and do not address the band's questions often along with these acknowledgement queries. Any essential controlled learning processes are carried out, but far from the conscious robots of science fiction. Facebook propels banks who can retain an outsider advantage (Facebook Messenger), framework (e.g., Barclays LaunchPad) and destination, on the opposite, chatbots, would implement the properties invention phase.

Tools and Techniques

AI strategies for banking authorities will quickly determine the consumer purchasing trend. Banks can enforce custom investment plans that directly influence consumer budgeting plans. The bank advises consumers of their data-based expenditures and expenditure. AI technology allows you to monitor conventional data and other data sources to consider consumer behaviour and desires, which will boost the employee experience. An immense volume of data and trends was carried out by artificial intelligence that could elude human observers. To deter theft, artificial intelligence may play a significant role. Several financial services companies are interested in creating artificial intelligence and in-depth learning solutions to detect real-time fraud. Internet banking, mobile banking, and artificial intelligence strategies are becoming commonplace as the process offers 24-hour transactions. In addition to these comprehensive demographic histories of interactions on the internet and offline and website research, machine learning processes are correctly implemented and analysed.

Outcome

In banks and financial companies, artificial intelligence methods are commonly employed. Based on this article, artificial intelligence as a philosophy to emulate human brains was developed. AI technology enhances consumer engagement and experience, improves banking operation productivity and establishes protection and risk management. This paper concludes the value of artificial intelligence in the banking sector. Knowledge AI offers banks and financial companies with information on cyber assault and solutions costs. Artificial intelligence methods recognise different problems about manipulation and privacy abuse.

Reflection

This paper focuses on the security applications of AI when it comes to banking. This is particularly important in understanding towards the complete impact of AI in banking.

Research Article 5 - Relationship banking and information technology: The role of artificial intelligence and FinTech

APA7 Reference

Jaki?, M., &Marin?, M. (2018). Relationship banking and information technology: the role of artificial intelligence and FinTech. Risk Management, 21(1), 1-18. https://doi.org/10.1057/s41283-018-0039-y

Aims and Research Question

AI supports banks in the privileges of big businesses with their small to medium-sized firm's clients. It is possible to personalize wealth management goods and services utilizing the new AI and computer-training techniques. New strategies for assessing and minimizing credit and operating threats using AI are being established after the financial crisis. With the developments in cognitive AI, banks are now able to support their clients effectively, efficiently, paperlessly and customized.

Background Research

Banks introduce machine learning strategies to solve these issues, for instance, local textual hacking, and leaving the payments. This is attributed to better language recognition skills, such as spelling mistakes in names or the flipping of digits. As the number of transactions is relatively high, and the scope for errors is never-ending, machine learning is a productive way of solving this.

Methodology Employed

The banking business is one of AI's early divers. The gradual introduction of AI into the banking sector primarily results in specific databases and the possibility of secrecy. However, as online banking becomes more common as a tool for the transaction round the clock, we expect AI to penetrate deeper soon.

Tools and Techniques

The market concept pursued by the organizations or their branches in the consumer finance field is funding purchasing customers' needs for personal spending or the purchase of durable products and properties and collecting service fees and interest. When a card is passed to the POS terminal of a supplier, the card and the sum of payment. Information will be transmitted by acquirer bank through the card association network to the card issuer for authorization. By submitting an approval letter or rejecting a message to the dealer in the same way (on card association network to purchase bank and thereby eventually the retailer) the issuer verifies the card and authenticates the charge.

Outcome

Cognitive AI innovations are used for improved services to consumers. Modern and sophisticated machine learning methods are used to personalize the different related banking products and sell them. Current computational approaches are being utilized to prevent crises such that banks are at ease in periods of credit tension.

Reflection

This article delves into the specific details of the relationship between fin-tech and AI and show the ways we can implement AI in the fin-tech industry to get results.

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