The banking industry is highly competitive. It is sensitive to political and economic conditions in their domestic countries and all over the world. Because of a lot of risks, a key strategy for many banks is to improve their performance by reducing costs and increasing revenues. One of the best ways to realize both objectives is to use data mining to extract valuable information from customer data.
The objectives of our present research is to define:
- What business strategies are best solved using Big Data analysis and Data Mining
- How banking executives can evaluate these strategies according to two criteria: (1) significance for business and (2) urgency for refining current technologies.
Top banking tasks that benefit from data mining
After examination of many strategies about “how to improve business performance in banking”, we defined the most significant tasks, and classified them into four groups. In each case, collection of more data can lead to significant improvements in performance. Here they are:
Customer experience management
This group consists of strategies that allow getting new knowledge about customers’ preferences from available analytics. Banks already have a variety of data about customers. In addition to personal information and data about accounts and transactions, banks can collect data such as purchase histories, channel usage, and geo-locational preferences. After analysis, the data can be used to create “the right order” at “the right time” at “the right place” for every customer rather than offering products and services based on what the financial institution would like to sell.
Market analysis and customers insight
In context of our current article this wide group includes relatively new strategies for market analysis and customer insight based on gathering and processing data from the Internet. The main task is to get valuable information from, for example, web click streams on the bank’s corporate site or customers’ social media interactions. This information can be used to attract new customers, increase loyalty of current customers, and gain competitive advantage due to a deeper understanding of market tendencies and customer preferences.
Study of channel performance
This group combines two approaches. The first one is using all data from banking channels in a more efficient way with the aim to increase their profitability. The second approach is using all channels to provide the “next best offer” for every customer in the most convenient way.
Credit scoring systems and fraud protection techniques are well-known applications of data mining analytics in banking industry. A modern trend in this group is extending the volumes of information that is used as predictors in data dining models. Social media interactions, transactions, purchase patterns and so on could be used as additional sources of information in risk management.
We now know what business tasks can benefit from data mining (and how). But where to start first? We next asked some banking experts their opinion on which tasks are most vital to achieving lower costs and higher revenues.
Experts’ evaluation of top business tasks
In the second phase of our research, we asked 25 banking executives, who deal with different retail segments and risk management, to evaluate the importance of each group of tasks described above. The finance sector experts evaluated each group based on how significant each group was to the banking sector and how urgently changes were needed. They weighted these two criteria and ranked the groups on a scale of 1 to 20, where 1 was the most urgent and significant, and 20 – least urgent and significant. The group with the lowest score is the most urgent and significant set of business tasks banking executives should focus on:
According to our research, customer experience management is the number 1 most significant and urgent topic in modern banking. Implementation of data mining in this set of business tasks is the best way to achieve customer centric banking and improve cross-selling and up-selling.
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Featured image courtesy of IOT Solutions World Congress.