- Data analytics in banking used for detect and optimize the risk management, understand the customer, peril and fraudulent activities. It optimizes the banks fragment, purpose or goal, obtain and hold the customer.
- In banking, data analysis techniques can be used by the institution and corporation can take the better decisions and also used for attest and discredit the already present model. Data analytics required with the science of evaluating the data to presume the data.
- Focus of data analytics is deduction of which is the process of deduces the data depends upon the data that already knows by the researchers.
- Data Analytics has many applications in our real – life time and some of the applications are Healthcare, Banking, Game, Travel and so on.
- Data analytics has different tools and some of the important tools are R and python programming, Tableau public, sas, Apache Spark, Excel, rapid miner,knime and so on.
- Data analysis techniques are
- Mathematics and statistic techniques.
- Artificial intelligence technique and machine learning technique.
- Visualization and Graphs techniques.
- Data analysis has many examples like transportation, detect the risk and fraudulent activities, web search and so on.
Types of Data Analytics:
- Data analytics is majorly categorize into 4 types and that they are
- Descriptive Analytics (what is occur)
- Diagnostic Analytics (why it’s occur)
- Predictive Analytics (predict what is going on to occur)
- Prescriptive Analytics (suggest action depends on predict)
- Descriptive analysis or statistic method can encapsulated the historical data and change into human readable or understood able form and also says what’s occurring. This analysis explain in elaborate about the present and past event occur.
- This analysis is mostly used by the companies and organizations. Some of the method of descriptive analysis is observation, case study and survey.
- Diagnostic analysis is used the past experience what’s occur and combine with diagnostic analysis why it’s occur. The outcome of this analysis is an analytic indicator panel.
- Regression method and some of the algorithm which are in training, these types of diagnostic analytics.
- Predictive analysis helps the companies and organizations in business to forecast the upcoming popular event depends on the present event.
- Predict what is going on to happen in future. Use of statistic and analytic model and machine learning methods are achievable outcomes in the predicted way.
Prescriptive analysis is a suggestion on measure of direction or route or track to examine data. Prescriptive analysis could be an order or step by step procedure of the forecast analysis.
Role of Data Analytics in Banking
- Data analytics is utilized in many applications in our real – life time and therefore the banking field is one of the real time applications of analytics of information.
- Data analytics is an activity of basic constituent that demand for enterprise. It incorporates a automate within the mechanical process and algorithms for fresh data. Some of the important role of data analytics in banking are
- Fraud detection.
- Risk management.
- Banks in predictive analysis.
- Customer feedback.
- In banking, data plays a vital role. Banking field use the data analytics for detect the fraudulent activities. Banking is a digital field and all the banking functions are done by the customer in net center or in home itself.
- Bank use the data analytics to avoid or detect the fraudulent activities, when the banks secure the customer data while online transaction.
- Analytics of data is helpful for us to rescue the price, time and satisfy customer and expect the risk intricate.
- In banking, data analytics is to determine and efficient way to monitor the chance and reduce the vulnerability to risk. Banking field is pursuing the new technology and more creative method to direct risk.
- The different types of risk are Credit risk, market risk, operational risk, liquidity risk, reputational risk, systemic risk. But, risk majorly categorized into 3 types and they are,
- Business risk
- financial risk
- Non – business risk.
Banks in predictive analysis:
- Predictive analysis defines that they predict the data by their forecast depends upon the data.
- By predictive analysis, companies can take better decisions and helps to reduce cost and improve user experience. This could be attain by use the varieties of types of data processing, storage, theory of game, machine learning techniques are used to make predictions.
- In banking, customer feedback plays a significant role. Using predictive analysis, banks and finance should maintain the great relationship with customer by providing services of customer and satisfy customer needs and provides preference for customer individually.
- It is also used to make better decision and perception makes on strategy.
Data analytics in banking can optimize the satisfy needs of the customer, predictive analysis for reducing cost and improve the user experience of the customer there upon the particular bank. Also detect the risk management, best service for customer, fraudulent activities and so on.