Technology Implementation in Financial Services

Introduction 

Technology is one of the aspects that are changing the world significantly in the current decade. It has been adopted in various sectors such as medicine, scientific research, manufacturing and transport sector. However an area which has seen substantial adoption of technology is the financial sector. Banks and financial institutions have become increasingly reliant on information technology to support growth and expansion into the global market and also to increase efficiency and security in the sector. The technologies which are highly associated with this sector are internet banking, smart cards, credit cards, automated teller machines (ATM’s) and telephone banking. However, there is a significant shift in technology which has resulted in the introduction of other technologies such as cryptocurrencies, big data analytics and machine learning. These are the technologies which have been widely adopted in the financial sector and banking industry as companies under this sector seeks to position themselves well. These technologies allow the customers to perform different routine transactions efficiently. It also makes it easy for the banking institutions to conduct their activities easily since they can understand the needs to their customers and address them at the right time. 

Applications of Cryptocurrency in Banking and Financial Services 

Blockchain technology is one of the most discussed topics in the financial services industry today. This is among the technologies which have come of ages and is among the most sophisticated aspects that need a thorough study before being applied anywhere. As time goes by, people are beginning to understand what blockchain technology is and are increasingly starting to appreciate its capabilities. However, how it can be harnessed and used best within business is among the uncharted waters that scholars are currently trying to exploit. Most parties in the financial and banking sector already have an understanding of the cryptocurrency concepts such as bitcoins, Litecoin among others. These are the concepts used in blockchain technology; a digital distributed ledger which has identical copies which are maintained on the networks of the member computers. With this technology, all parties can review previous transactions and record new entries. It also allows transactions to the grouped into blocks and recorded one after the other in a chain of blocks (blockchain). The links between respective blocks and their content are protected by encryption so that the transactions cannot be destroyed or forged by malicious individuals. This therefore means that the transactions in the network and the ledger are trusted and has no middleman or the central authority. There are various applications of blockchain technology in the financial and banking sectors. These applications are discussed below. 

Speeding up and simplifying cross-border payments 

The process of transferring funds has always been an expensive and slow process. This is even worse for cross border payments or fund transfers. However, with blockchain technology, the speed of moving funds across the border has been made as painless as possible and has also led to a significant reduction of transfer costs (Boersma, 2018) . Companies such as Royal Bank of Canada (RBC) has in the recent past conducted a pilot test on the use of blockchain for cross-border fund transfers and payments mainly to and from United States as part of the existing payments. The technology that RBC used was created based on the open source technology which is maintained by the Hyperledger Blockchain consortium. The technology allowed monitoring of payments as the pass between the Canadian and US banks in real time. The technology according to Martin Wildberger the bank’s vice president is cheap and has reduced losses. Furthermore, the distributed ledger technology can significantly improve and simplify the general functionality of the banking systems. 

As time goes by, there are other projects that are being advanced involving blockchain technology. The technologies that are being experimented with blockchain include loyalty offerings and consumer rewards (KEITHSON, 2018) . There are however some stumbling blocks to blockchain technology in financial industry such as security issues. However, successful integration of this technology to the payment systems is highly promising. 

Share trading 

Blockchain technology has shown high potential in the future of share trading. With blockchain technology, trade accuracy is increased and the settlement process reduced immensely. The use of distributed ledgers delivers real-time clearing and settlement of trades as investors will have to pre-fund brokerage accounts to place trades as cash debits which would allow orders to be matched accordingly. The seller of the shares would be instantaneously receiving cash funds into their settlement accounts. Secondly, the blockchain technology significantly reduces the brokerage fees as a result of intensified competition resulting from low entry barriers. 

Smart Contracts 

Smart contract is one of the most promising applications of the blockchain technology. This technology allows execution of commercial transactions and agreements automatically. Furthermore, blockchain technology allows enforcement of obligations for all parties involved in a contract without necessarily incurring added expenses of a middleman. Blockchain technology also eases the process of online identity management which is required for online transactions. When blockchain technology is used in identity management, users can easily choose how to identify themselves and the people that need to be informed. Although users are required to register their identity in the blockchain, they can reuse this identification for other services. 

Machine Learning 

Machine Learning (ML) has had fruitful applications in the finance and banking sector even before the advent of other forms of technology such as mobile banking applications, search engines and proficient chat bots. It is however currently on the verge of making the biggest impact on the banking industry. Due to the high volume of data, need for accurate historical records and qualitative nature of the finance world, artificial intelligence is required to provide solutions to some problems that might be encountered in this area (Fedak, 2018) . The advancement of technology and the increased complexity of finance and banking sector has made machine learning necessary than ever before. This trend is perpetuated by the accessibility of more computing power and machine learning tools. 

As a result of the complexity of banking and finance industry, machine learning has to play an integral part in the financial ecosystem. From loan approvals, asset management to assessment of financial risks, ML has aided financial institutions become one of the techno-savvy institutions in the world today. However, just a few individuals know accuracy and the number of ways ML can be applied to financial institutions. Below are some of the applications of machine learning in banking and finance industry. 

Portfolio Management 

Machine Learning (ML) has helped professionals and banking institutions in managing portfolio of different users. Robo-advisor for instance is a commonplace term in the financial landscape in the current decade. These are algorithms which are built to calibrate financial portfolio and risk tolerance of the user. It allows users to enter their goals, financial assets, age and the system spreads the investment across asset classes which allow it to reach the goals of the user. It thereafter calibrates to the changes made by the user on their goals and in accordance with the real-time changes in the market and finds the best fit for the original goals of the user. This technology has gained a significant grip in the financial sector since the users of this technology does not require a physical financial and investment advisor to provide guidance on investment and reduces the need for fees that would have otherwise been paid to the human advisors. 

Fraud Detection 

As a result of the accessibility of the computing power and internet, financial institutions have become a target for fraudsters who attempt to carry out various fraud operations. This is also made possible by the storage of company data in the cloud which subjects data to potential attacks. Although the previous fraud detection mechanisms worked, they could not defend the financial institutions from attacks effectively. They depended on robust and complex sets of rules which were rigid and were not comprehensive enough. However, with machine learning, fraud detection goes beyond specific sets of rules and follow particular sets of risk factors in the process of learning and calibrating new potential threats (Faggella, 2018) . Machine learning systems are capable of detecting unique activities or behaviors commonly known as anomalies and flags users that show such anomalies altogether. The security team can later analyze individuals that have been flagged and tries to figure out if they are false positives or not. This technology is highly accurate and has the capability of analyzing more users than the previous technologies. 

Loan or insurance underwriting 

Underwriting is one of the tasks that machine learning is capable of performing perfectly in finance sector. Despite the fact that there are many worries that the near perfect manner in which ML is carrying out operations would likely lead to loss of employment opportunities, the perfection in which it carries out operations leaves no chance for failures which were prevalent before. In large banking institutions, insurance companies and other financial firms, ML algorithms can be trained basing on millions of examples customer data, financial and insurance results. The algorithms can later be used to assess the underlying trends continuously to detect the changes which might influence lending and insurance in the future (Faggella, 2018) . This has provided a tremendous yield for the companies although it is currently being used mainly by the large companies that have resources and are in the capacity to hire data scientists who can study large volumes of past and present data and train the algorithms on the same. 

Algorithmic Trading 

Algorithmic trading sometimes known as Automated Trading Systems has an extensive history which dates back to 70’s. It involves the use of complex Artificial Intelligence systems which makes the process of trading fast as it enhances decision making process. The algorithmic systems make thousands or even millions of trades which is perfectly described by the term “High Frequency Trading” (HFT). HFT is the subset of the algorithmic trading. ML plays a critical role in the calibration of trading decisions in real time (Eisenberg, 2018) . Despite the limitations of this technology, it has more advantages in the financial sector as it significantly improves performance and reduces the challenges that were there before. 

Investment Prediction 

Computer aided trading is a paradigm which has been there for some time. It allows individuals/investors to have an order placed when stock reaches a given predetermined price and also allows them to sell when the price drops below a given limit. Through automation of functions, these platforms make trading hustle-free for both large and small investors. These systems also make recommendations to the individuals and carry out automated analysis of the market trends. Many financial institutions have moved away from traditional analysis methods and have embarked on automated machine learning algorithms which help businesses predict financial trends. With machine learning algorithms, financial bodies are able to identify changes in the market quickly and more effectively than using traditional investment models. Companies such as JPMorgan and Bank of America are among the institutions which use machine learning to automate functions. 

Despite the advantages that are offered by the machine learning, it has some limitations one of which involves the need for specialized individuals to study the large volumes of data and train the algorithms accordingly. However, machine learning is likely to improve various aspects even more in the future more than it has been seen before. The adoption of Chat bots and conversational interfaces are likely to expand the area of venture investment and improve customer service while significantly reducing the budgets. 

Big Data in Finance and Banking 

As stated earlier, technology plays a critical role in the evolution of the banking and finance industry. The provision of services and the way the banks operate have substantially changed for the past few years owing it to the advancement in technology. The advent of the big data revolution changed the banking and finance industry for the better. The industry has realized the opportunities associated with this technology and so have the other industries. Finance institutions in most cases have huge information stored in their databases. However, most of these companies have no clue on what to do with the massive amounts of data that describes the history of the organization and customers. However, the entry of Big Data has unlocked a number of ways in which large amounts of data can be turned into a fortune both for the company and the customer. A large company can store up to several terabytes of data which makes it hard for analysis to be conducted and trends discovered. However, big data has bolstered various areas of the industry. 

Learning patterns of the customers 

With the help of customer withdrawal and deposit as well as payment data, banks and other financial institutions are capable of understanding the customer spending patterns. This helps the organizations in the discovery of when the potential customers might require specific financial services so that they can tailor these services accordingly. With big data analytics, the company can see different customer spending patterns which are differentiated by various aspects such as demographics, the average income among others. The customer spending patterns made possible by big data analytics helps identify the customers that are highly valuable by identifying those groups who spend more money than the others. Through this knowledge and data, the banking institution can provide financial offers which help people under this category to feel valued as a customer. Additionally, Big Data can be used to detect high risk spending patterns which are likely to affect customers adversely in the long run and guard the customers from losses that might be incurred. 

Fraud Detection and Prevention 

Financial institutions have in many times been targets to fraud activities from different individuals. However, banks have always remained aware of the fraudulent activities and have been seeking ways to mitigate them. Considering the fact that fraud has always been one of the biggest challenges in finance sector, different methods have been tested to try and find out the best way of handling these problems. However, big data is the catalyst of the process of detection and prevention of fraud as it helps banks to reduce process time for detection and prevention of the potential fraud. With Big Data, a company is capable of mining useful patterns which help raise security standards of the finance industry therefore ensuring financial institutions easily mitigate any fraudulent activity before they become big and turn into a disaster. 

Easing customer segmentation 

Insurance companies, banks and other financial institutions have different types of customers with unique requirements from the institution and different financial behaviors altogether. It is often hard to address the demands of each customer due to the complexity and diversity of each demand. However, with the help of Big Data, financial institutions can group their customers into various categories based on their behavior and other parameters which differentiate them (Mauricio, 2016) . Putting them on different segments benefits them during time of marketing promotions as they could easily target a specific group of people according to their demands which will be of benefit to them. This helps build a customer relationship between the institution and the customers. 

Risk Management 

Risks in banks and financial institutions are a major part of operations. However, high risks are dangerous and are capable causing losses some of which can drive a company out of business. Risks in financial institutions include bad loans, fraud, failed investments among others. With the help of big data analytics, an organization can get the intelligence required, detect potential risks earlier and act on them decisively to help prevent possible losses which may otherwise be incurred. With big data, the analysis of problems and risks on a large scale can be conducted with the help of analytics which divides problems and large volumes of data into smaller bits which are manageable. In the long run, big data is beneficial to both the financial and banking institutions and the customer. 

  Enhanced Compliance Reporting 

Currently, banks have an access to millions of customer needs and can now use Big Data to cater to the needs of each one of them effectively. The cloud based analytics packages for instance can synchronize with the big data systems in real time thereby creating actionable insight which can be used in a meaningful way. Big data allows expansion of banking industry in a manner that the institutions will be able to earn more revenue as a result of the reduced cost. Additionally, through cutting down of unnecessary costs, the banking or finance institution will provide customers with their needs instead of having to go through a series of irrelevant information. 

Personalized Product Offerings 

Another important way in which big data can be used in financial or banking institutions is helping banks come up with personalized services that are aimed directly at the customers. Through analytics and the study of customer behavior, the present as well as the past outcomes through Big Data analytics, an institution is capable of identifying the ways in which a customer can be retained and how new ones can be attracted as well. Also, banking institutions can gather untapped information and niche and make personalized products and services to suit their customer. 

Comprehensive Progress Evaluation 

Data analytics has made it easier for monitoring and evaluation of the progress of banks which have been entrusted with sensitive information belonging to the customer. However, with big data, banks can now use the information to constantly monitor the transactions of different client’s transaction behaviors in real time. This allows them to provide the resources that are needed by the customers. This boosts overall profitability of the company as it reduces the cost of production. With the increase in the volume of customer data, the services offered could be affected. However, switching to Big Data allows the businesses to process information much faster and allows them to avoid potentially difficult situations altogether. 

Generally, the adoption of technology has significantly changed the way in which businesses operate. Due to the way in which various innovations change businesses, many companies have rushed to invest in different technologies such as Big Data, Machine Learning and Blockchain technologies. With the help of these technologies, businesses are able to deploy highly focused products and services. Innovations currently target the intersection areas that frustrate customers and high profitability for the company therefore allowing them to develop high quality products which not only meet the needs of the customer but also ensure that the company makes profit. Traditionally, banks and financial institutions have charged very high fees for transfer of funds across the border. However, with the advent of information technology, the fees have dropped significantly and the cost of operations has also been lowered. On the other hand, automation has reduced the cost of operations and increased profit margins. Furthermore, it has enabled companies to use data strategically in decision-making in areas such as lending and investment since data can be modeled using various applications and decisions made based on the results. 

Conclusion 

In summary, companies have seen a lot of success due to the adoption of different types of technologies. Many companies have increased their profit margins and reduced the cost of operations. Additionally, technologies such as Machine Learning and Big Data eases the process of understanding the customer and allows companies to make accurate decisions after patterns have been learned accordingly. Although technology has numerous advantages most of which are advantageous to the company, there are some challenges that are presented. One of the biggest challenges that organizations face while implementing technology in their operations is the high costs involved. Machine Learning for instance requires a high level of investment on algorithms and personnel who can analyze large volumes of data and train the algorithms based on the patterns obtained. Despite the cost implication of technology, it is worth it as it reduces cost of production, risks and increases profits. 

References 

Boersma, J. (2018, 4 9). 5-blockchain-use-cases-in-financial-services.html . Retrieved from https://www2.deloitte.com: https://www2.deloitte.com/nl/nl/pages/financial-services/articles/5-blockchain-use-cases-in-financial-services.html 

Eisenberg, A. (2018, 4 9). machine-learning-in-finance/ . Retrieved from https://igniteoutsourcing.com: https://igniteoutsourcing.com/publications/machine-learning-in-finance/ 

Faggella, D. (2018, 3 27). machine-learning-in-finance/ . Retrieved from https://www.techemergence.com: https://www.techemergence.com/machine-learning-in-finance/ 

Fedak, V. (2018, 1 22). 5-use-cases-of-machine-learning-in-the-banking-industry-a4cfbedda722 . Retrieved from https://techburst.io: https://techburst.io/5-use-cases-of-machine-learning-in-the-banking-industry-a4cfbedda722 

KEITHSON, J. (201, 9 28). royal-bank-of-canada-pilots-blockchain-tech-for-cross-border-fund-transfers/ . Retrieved from https://www.banklesstimes.com: https://www.banklesstimes.com/2017/09/28/royal-bank-of-canada-pilots-blockchain-tech-for-cross-border-fund-transfers/ 

Mauricio. (2016, 5 3). role-big-data-banking-industry/ . Retrieved from http://bigdata-madesimple.com: http://bigdata-madesimple.com/role-big-data-banking-industry/