Impact of Data analytics over the Banking Industry (global)
Answer. Data analytics can be defined as a scientific process for scrutiny of raw data to gather conclusive patterns and reports with respect to the desired contexts. The use of data analytics in business owes its origin to the need of analyzing collected data by business for finding patterns, trends, correlations and cause interpretation regarding business practices. The primary challenge of data analytics comes in terms of the speed, variety, and size of data gathered by business and required analytical tools for facilitating decision making on the basis of such analytics. Hence, we can say that along with the advent of technology, there has been a parallel evolution of data analytics in business and the widespread use of data analytics leads to a subsequent rise in consumer preferences, behavioral trends, requirement analysis, product manufacturing and warehousing requirements, etc for various types of business. For example, the products offered by banks in developing countries like Indian have grown over the last few decades and this has led to a rise in data collected from all the products and the banking industry have adopted various data analytical tools to reap the most out of these data (Analytics India Magazine, 2015).
Analyze the main advantages and disadvantages of using data analytics within the industry or company that you have chosen.
Answer. The use of data analytics in the banking industry has more of advantages than the disadvantages. The prime reason for this is that results from the analytical tools give a better insight over treating customers as per their preferences and also ensures profitable operations for the bank by maintaining the security of the transactions and controlling the portfolio trends (Shankar, 2015). The main advantages from using data analytics in global banking industry are as follows (Deloitte, 2015):
1. Enhanced fraud regulation and compliance monitoring facility from the analyzed data.
2. Better transparency in business processes and calibration of risk metrics to ensure safe lending.
3. Provides cushion to drive the banking business within risk adjustment parameters.
4. Higher adjustment capabilities to manage post fraud scenarios.
5. Easier quantification of customer satisfaction indices and operational profitability.
6. Generating leads on higher-potential enabled customers.
7. Customizing the prime offerings for customer preferences via product, pricing and promotion
8. Informed decision making by the senior management.
The main disadvantages of adopting data analytics in banking industry are as follows:
1. Initial costs of additional IT infrastructure for setting up data analytics.
2. Consequent dependency of decision-making process on data analytics results in banking
3. Incremental budgeting required for matching analytical expert professionals
4. Delayed response timing in big data streamlining and scope of periodic error in big data sorting.
5. Higher costs of analytical tools deployment into the conventional banking system.
3. Determine the fundamental obstacles or challenges that business management (in general) must overcome in order to implement data analytics. Next, suggest a strategy that business management could use to overcome the obstacles or challenges in question. Provide a rationale for your response.
Answer. The inclusion of data analytics into conventional banking systems is a very tedious process as a number of challenges have to be overcome for enabling this transition. The main challenges are:
1. Deciding the area of focus for applying analytics: This refers to the decision of prioritization among available pool of data and the deciding what aspect should be first focused upon, viz. customer preferences, risk mitigation, lending compliance, portfolio quality etc(Deloitte, 2015).
2. Streamlining data as per chosen domain: This refers to finalizing what factors and data mines need to be considered analyzed to get the required analytics for the chosen domain.
3. Synchronization of analytical insights with hierarchical management decision systems (Deloitte, 2015).
4. Finding right human resources to meet the analytical tools like statistical modeling, small data analytics, big data analysis, etc. The recruitment management should include a talent acquisition according to the newer system requisites for internal and external resources.
5. Enhancing coordination and seamless flow of analyzed info across the departments- The banking industry has various domains like branch banking, operations, risk management, etc and all the above should be in sync for better decision support over the chosen data domain.
The following strategies are suggested to manage the above-stated challenges:
Hire third party experts for deciding the precise point of data analytics application and ensuring adequate data streamlining measures to meet this. The rationale behind this is to develop an unbiased prioritization for probable domain of applying data analytics.
Developing an outsourced specialist channel (with confidential integrity clause) to manage all the data analytics and ensure proper coordination with the consequent management decision systems. The rationale behind this is to avoid multiple distractions into human resource add-on.
Strategizing and linearly training the various departments to endorse and ensure seamless flow of analytical information and its application into real time product /services variations. The rationale behind this is to administrate thorough percolation of analytical insight across the banks and maintain a strict internal mandate of applying them into practice.
4. Analyze the overall manner in which data analytics transformed the industry or company you selected with regard to customer responsiveness and satisfaction.Answer. The global banking industry dwells specifically around customer satisfaction and the prime utility of data analytics comes in field of analyzing customers’ transaction data and developing preference metrics for them (Crosman, 2015). The banks are nowadays developing their digital presence to maintain a thorough link between their customers’s buying behavior and their ultimate needs. Leading international banks have been augmenting their customers’ online transaction patterns with their social responses to family events like house renting, buying, gifting etc. For example, RBS in USA has already excelled the art of correlating customer buying behavior by mapping their online spending patterns and their observed social life responses in social networks (Crosman, 2015). It can be also ratified that big banks have already started using data analytics to boost customer satisfaction in following ways:
1. Optimizing products specific offers and bundled cross selling on the basis of preference mapping.
2. High end grievance addresser and satisfaction reflecting problem solutions (Martin, 2015).
3. Preselecting probable frauds in payments and investigation for delinquencies (Martin, 2015).
4. More personalization of investment offers and highly customized saving schemes based on trends.
5. Speculate on the trend of using data analytics for the chosen industry or company in the next ten (10) years. Next, determine at least one (1) additional type of data that one could collect by using data analytics. Provide a rationale for your response.
Answer. Currently, inclusion of big data analytics has been a tough challenge for smaller banks due to higher costs of operations and corresponding efforts required for talent acquisition. However, over the period of next ten years this situation will be resolved in form of a democratization of technology in data analytics. The coming future will have data analysis via cloud technologies which will offer easier and quicker access to analytical tools at an affordable cost (Yurcan, 2015). Thus, this revolutionary change of democratization of technology will magnify the use of data analytics for all types of banks as their assets growth rates are also increasing at almost 100% rate (Renoux, 2015).
An additional data type which can be collected in banking industry can be the result of earlier lent loans and this can be used as a benchmark for automating the lending risk process. For example, the various factors which affect a loan default are those of client financial, client’s industry, loan type, loan amount, loan tenure, rate of interest, etc and all these factors in earlier successful loans can be analyzed to map a risk metric. The rationale behind this metric is to define the ideal combination of factors which can be checked for averting default and improving their financial statements (Utkarsh & Santosh, 2015).
References
Analytics India Magazine (2015). Analytics in Indian Banking Sector – On A Right Track. Retrieved from http://www.analyticsindiamag.com/analytics-in-indian-banking-sector-on-a-right-track/
Crosman, Perry (2015). Can Big Data Recreate Personal Touch of Bygone Banking? RBS Thinks So. Retrieved from http://www.americanbanker.com/news/bank-technology/can-big-data-recreate-personal-touch-of-bygone-banking-rbs-thinks-so-1077375-1.html
Deloitte (2015).Analytics trends 2015 .Retrieved from http://www2.deloitte.com/us/en/pages/deloitte-analytics/articles/analytics-trends-2015.html
Martin (2015). How Big Data Changes the Banking Industry. Retrieved from http://www.cleverism.com/big-data-changes-banking-industry/
Renoux, Terry (2015). 2015 Lending Outlook: Close the Customer Experience Gap. Retrieved from http://www.banktech.com/core-systems/2015-lending-outlook/d/d-id/1318555?
Shankar, Vijay (2015). Big Data Analytics and Customer Service in Banking. Retrieved from http://www.247-inc.com/company/blog/big-data-analytics-and-customer-service-banking
Utkarsh, S. & Santosh, G. (2015). Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks.Science direct, 50, 643-652.
Yurcan, Bryan (2015). Why Small Banks Should Be Thinking About Big Data. Retrieved from http://www.americanbanker.com/news/bank-technology/why-small-banks-should-be-thinking-about-big-data-1077955-1.html
