Use of AI in DBS
Question:
Discuss About The Artificial Intelligent Financial Evaluation?
The report is based on analyzing the use of artificial intelligence (AI) in selected banking organization, The Development Bank of Singapore (DBS). The banking sector is exploring of adoption of the artificial intelligence into the operational processes. By adoption of the artificial intelligence, the banks are automated their operational processes which result into reduction of cost and faster the turnaround time. The Development Bank of Singapore is looking forward to upgrade the operational processes (Moro, Cortez and Rita 2015). DBS is adopting of artificial intelligence in order to blend the digital banking. The bank is focused on development of ledger system such that it can enhance the financial services. Artificial intelligence into DBS bank enhances level of banking which provide the customers capacity to use banking services.
The purpose of this paper is to analyze the artificial intelligence and digital innovation into banking sector which enhances the customer value. The report discusses the product and processes roadmap by outlining of the minimal viable product and process. It also develops the cost benefit analysis for the artificial intelligence. It also discusses the short term strategy as well as long term strategy to launch of the MVP (Minimal Viable Product) and then validate the product.
The different short-term strategies for launching MVP and validate products are discussed hereafter. It must be reminded that time and money is the key barriers keeping one from testing amazing business ideas.
- Creating a fake brand:
The Development Bank of Singapore or DBS must consider their brand name. During testing phase, their MVP is needed to be provided with a fake name. This must be descriptive enough for resonating with customers (Copeland 2015).
- Setting up shop:
DBS must possess a branded website. The website should come with its distinct template and an effective back-end editor. This must be quickly figured out. The bank must plug in their domain URL and logo to new sire. Then everything like logo and brand name must be set up on different social media sites.
- Stocking the shelves:
DBS must write creative service descriptions for their site. This must help potential customers to witness every appeal of their service. This must also include technical specs (Ahmadi et al. 2014). It is useful to make up few details that must gauge the response of customers to their potential services.
- Disrupting and differentiating:
The different odds to close the sale rise as the target audience has been drawn in through a unique perk. This is presented by value proposition. Completion of a transaction at many times hinges on the way how successful the benefits have been in engaging various clients (Brynjolfsson, Rock and Syverson 2017).
Short-term and Long-term Strategies for MVP Launch
The main mission to implement AI at DBS is to develop opportunities regarding faster and more personalized customer experiences. This is intended to retrieve better insights and automation of back-end flows. DBS nurtures a clear vision of different optimistic signs of adoption and interest even under stodgy banking incumbents. Various discussions across media regarding the emergence of AI have been ranging from automation and their efficiency to cut numerous jobs to the startup acquisitions (Kaznacheev, Samoilova and Kjurchiski 2016). Thus DBS expects an intuitive, multichannel and easy-to-use tool are maximizing customer experience and reducing time.
One of the goals in the current case is anonymity. Since intelligent algorithms go for data pools automatically, developers never need to personally access data, create patterns manually and analyze that. Further another goal is to communicate proactively with the information needed by customers before they call contact centers (Stobbs, Hunter and Bagaric 2017). Next, an important objective is the administration. DBS provides tags to simplify and help financial handling. Till date, users gas to tag transactions manually. However, AI optimizes this process. For all this, the most effective strategy has been to focus on research towards automated tagging of transactions. Hence, testing various AI options for creating the initial prototypes is important tactics.
The different hypothesis includes the following.
- Lack of efficient AI applications used by employees and customers at DBS.
- Lack of benefits regarding time, effort and cost reduction.
- Lack of clear future was doling for AI at DBS.
MVP might misguide DBS to use their strategy to begin a new project. A strategy regarding quantitative and fast market testing is needed to be narrow and somehow misleading. The first strategy is proper focus (Riedl 2016). It is important in the current scenario avoiding a mainstream audience. Setting customer attention in a proper way and getting to a relevant audience is the first trial. DBS must remember that they are providing a vision of future product and idea of what simple tool that has been playing with currently can turn out to be.
Next, DBS must consider minimalism. They must try to supply ideas efficiently cost such that customers are ready to provide payment. They must balance their vision and customer expectations. During dealing with every kind of tasks, DBS must go for the proper platform for a cheap and quick start. Moreover, they should be flexible and closely interact with customers. This would help to utilize time over no-profit ideas (Bench-Capon 2014). Here one can analyze and understand better market needs. Then the testing should be considered. MVP testing is about making sense whether the supply is solving customer’s challenges along with a readiness to pay for that. Apart from this, testing denotes company resources and market capabilities.
Issues and Benefits of AI Implementation in Banking
Lastly, DBS must understand that during the implementation of a strategy, they must not fail. They must abide by some general approaches to escape different unhappy outcomes. They should use their intuition. Then, they must discuss with domain experts (Bostrom and Yudkowsky 2014). Next, they must begin with different universal risks.
The main mission of utilizing machine learning or AI at banking is to fight against fraud. The technology is also intended to improve compliance. It is ideally appropriate for challenges since different algorithms for machine learning combs through large transactional data sets spotting unusual behavior.
The primary vision is to develop software robots that must act as virtual workers. They can be trained mostly by business users in a very intuitive way (Castelli, Manzoni and Popovic 2016). DBS wants to free up their valuable and limited skills of IT experts to focus on more and more strategic tasks.
Since technology continues to forge significant changes in DBS, is must provide the organization through closing and small, interface to thrive their digital feature. This is their primary goal (Makridakis 2017). Further, the objective underpinning, in this case, is that the technology innovation must shape their industry, workforce and partnerships.
The strategy implemented must in such a way that it finds value within disruption and then deepens their role in the lives of customers. The best tactics are that DBS requires scaling back their dependability over legacy core banking systems and then implementing current IT platform. This is to create international standards along with related business processing regulations available.
The hypothesis is discussed below.
- DBS intends to use AI under heavily-manual process regarding cost benefits, speed efficiency and accuracy.
- There are various external and internal factors helping to elevate the customer experience and then move employee towards more judgment based and high value-added responsibilities.
The research provides a roadmap to implement the artificial intelligence into the banking sector. AI is adopted into the bank and it has potential in order to drive revenue, decrease into operational cost along with mitigation of risks. DBS invests in as well as deploys of artificial intelligence as it controls the market (Ogwueleka et al. 2015). The MPV of DBS is those products which satisfy the customers and then providing of feedback towards future development of product. Through the AI products, it consumes as well as processes of larger amounts of data. It speeds the efficiency of the financial services and then becomes more efficient.
AI products can detect the frauds by failing of the unusual transactions. It builds up trust and creates a customer value. The customers are attached to the bank which provides of personalized services. The banks are mainly used of AI technology to use of robots as the financial advisors, that provides automated as well as algorithm based financial. The financial products provide better productivity to make the financial operations more effective as well as efficient (Wu, Chen and Olson 2014). The workforces are focused on core banking functionalities and reduction of cost. DBS is a systematic along with data centric banks which is used to quantify along with minimizing of the risks and customer support, blocking of frauds, forecasting and identifying the new customers to market the new financial services (Cavalcante et al. 2016). The banking operations of DBS are highly data intensive as they are involved into opening of bank account, submission of forms and documents.
Conclusion
Apart from the benefits of artificial intelligence into the banking industry, there are also issues which are faced by DBS. There are potential error rate into AI due to technological infancy. The major concern is data security and lack of trust on the AI regulations. Therefore, data security is the main threat of new technology. Dunis et al. (2016) stated that important concern regarding the adoption of artificial intelligence is ethics as well as moral values. It is not right to install intelligence system into the machine and it works for the benefit of the bank. AI system should prevent the customer privacy, technological complexity along with loss of security control over business strategies. In the banking sector, AI system is focused on providing secured data to the customers so that DBS can gain of customer satisfaction and value (Pereira, Basto, and Ferreira-da-Silva 2014). Proper financial transactions are the main concern of the organization, as loss of data as well as information will cost a high loss.
One of the issues of AI is cost, as its creation is expensive. In the bank, creation of AI needs of huge cost as they are complex intelligence system. Their maintenance as well as repair of the system requires huge amount of money. The software programs are required to be updated to meet with changing environment of the customers (Copeland 2015). The procedures to change the lost codes along with installation of system require long time along with higher cost. The cost issues arias when it is related to implementation time, integration challenges, lack of understanding of AI along with its usability with other platforms (Zhu 2016). As there is higher cost of machines, therefore hardware as well as software requires more time and money to update in order to meet with the financial requirements.
Recommendations and Conclusion
Following are the recommendations of adoption of AI into the business operations of DBS:
Robust technology: DBS should use of robust AI technology for future benefit of the business operations in the financial services of the bank. It controls over the transactions, loans and implement of ledger system.
Secure the customer data: The stored data into the AI system should require to be kept secured so that no unauthorized person can able to access it without permission of the data owner.
Training program: In order to develop and build of AI into the banking industry, the bank should hire as well as provide training to the digital practitioners and experts to handle the financial services. It also enhances the long and short term strategies to launch of MVP into the organization.
Conclusion
It is concluded that artificial intelligence chosen for the bank, DBS helps to improve the financial operations along with satisfaction level of the customers. It is analyzed that artificial intelligence automated the financial decisions by assisting the users to take it. It monitors the financial events, stocks and bonds the price against the financial goals. The AI applications embedded the end users devices and financial institutions servers for capable to analyze the information and customized financial advices, forecasting in addition to financial calculations. Therefore, it is concluded that artificial intelligence has key significant importance into the banking sector to handle the financial services.
References
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