This includes demographic information, transactional archives, product holdings, service favorites, online activity logs, call center communications, social media and feedback including complaints and questions. An industry, that seems to be more grateful of the value of data is banking, it was suffering by lack of data limpidity during the 2008 crisis. Data analytics can help banks achieve several business outcomes such as: develop their customer base, maintain the most profitable customers, constantly improve operational efficiency, transform and automate financial processes, and identify and prevent fraud.
IT systems transformed virtually every single ann. process. Today, banks have that rare opportunity to reinvent themselves again with data and analytics. “Every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics,” says Toss Dorval, a director in Muckiness New York office. Advantages and disadvantages of using data analytics within banking. Data analytic provides financial organizations information about loans and credit reporting. Based on the model bank creates from historical customer’s data, the bank can decide good and bad loans.
In addition, data analytic protects credit card owners by assisting banks detect fake credit card transactions. Global Head of Strategy for HASH Technology and Services, said in a statement. “Programmer like Finance are essential for the healthy, thriving and developing financial services market. ” (Ranger, 2013) On the other hand, the disadvantage of data analytics is the processes needed to prepare for and conduct data analytics are complex and expensive and require expertise in statistics and modeling.
Add to that, using real time big data analytics is one of the best advantage for banking, but t requires a different way of working within the company. Real-time insights require real-time action. This will have an effect on the culture. (Irishmen, n. D. ) Challenges for implementing data analytics. Entrants agreed that the real issue is the expectations of senior management. Senior leaders’ expectations for paybacks are divorced from the realities of frontline application, privacy and security of the collected data; as media accounts have rightly pointed out excesses in some data-gathering methods.
Little wonder that consumer wariness has risen, companies that are blowing he skill problem through inventive employing and compensation strategies are facing shorthanded in people whose talents bridge the disciplines of IT and data, analytics, and business decision making, and getting managers and individual contributors to use new tools purposefully and enthusiastically is a huge challenge. (Brad Brown, 2014) The best strategy for any company is to function along two horizons: capturing fast wins to shape momentum while keeping sight of longer-term, the ground- breaking applications.
The journey goes through numerous horizons. In addition, companies must invest more in training to support more multifaceted analytics. Consider a tool for underwriting small and midsized business loans. Underwriters must training on fit the model into the underwriting process flow and how they integrate the models and tools with their own experience of customer characteristics and their business priorities. Data analytics and customer responsiveness and satisfaction. Customer satisfaction is a result of a perceptual and affecting valuation, where some judgment standard is compared to the actually vs.. Reference. If the perceived performance exceeds expectations, customers will be satisfied. Data analytics is significant to understand customers’ needs by the ability to effectively analyze customer data. Banks cannot define the level of service to provide, how to retain customers loyal and satisfied, and meet their future financial needs. As banks aggressively compete for valuable customers, lack of insight is very powerful. It influences revenue and profitability through decreasing share of wallet, ineffective marketing campaigns, high customer opt-out rates and diminished customer lifetime value. IBM, 2011) For example, online banking platform for a bank combines customer’s information from different countries where the same bank operates. Customers are now able to access a centralized view of their accounts, plus banks are providing services to their customers at anytime from anywhere “By improving our social media analytics capabilities, we’re engaging with our customers on a one-on-one basis, to address and anticipate their needs more cohesively,” Eugene Lieberman, head of Retail Business Intelligence Solution Science at Antenna, said in a statement.
Taft, 2014) The trend of using data analytics for banking in the next ten years. Any changes to the banking industry will be driven by increasing consumer demands collective with continuous competition from outside the industry. It is a universally believed that the financial services industry is improving very quick which will lead to that there could be several organizations left behind or consolidated due to a failure to reply to customers’ expectations and cost and revenue challenges.
Because of the impact of the agile start-ups and niche players, banks must think more like obsessing about customers’ experience and using digital technology to avoid being by-passed by new competitors or established challengers. This has caused development of innovation labs, finance investment funds and uncommon alliances between regular financial organizations and start-ups. “Banks should focus on adjusting to a new paradigm, a differing customer value proposition, and be open to a wide spectrum of burgeoning opportunities. ” ? Bradley Limier References Brad Brown, D. C. (2014, March).