Role Of Technology Upgradation and Its Impact on Banks (Jaiib/DBF Paper 1, Module C, Unit 4)
As we all know that is Role Of Technology Upgradation and Its Impact on Banks for JAIIB Exam. JAIIB exam conducted twice in a year. So, here we are providing the Role Of Technology Upgradation and Its Impact on Banks (Unit-4), BANKING TECHNOLOGY (Module C), Principle & Practice of Banking JAIIB Paper-1.
♦Trades in Technology Development
The Advancements in software tools, computer hardware and telecommunications have shifted the focus of the banks towards computerization from data processing to information services.
- Data Warehousing
- Data Mining
What is Data Warehousing?
- Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. Data warehousing involves data cleaning, data integration, and data consolidations.
Using Data Warehouse Information
There are decision support technologies that help utilize the data available in a data warehouse. These technologies help executives to use the warehouse quickly and effectively. They can gather data, analyze it, and take decisions based on the information present in the warehouse. The information gathered in a warehouse can be used in any of the following domains −
- Tuning Production Strategies− The product strategies can be well tuned by repositioning the products and managing the product portfolios by comparing the sales quarterly or yearly.
- Customer Analysis− Customer analysis is done by analyzing the customer’s buying preferences, buying time, budget cycles, etc.
- Operations Analysis− Data warehousing also helps in customer relationship management, and making environmental corrections. The information also allows us to analyze business operations.
Integrating Heterogeneous Databases
To integrate heterogeneous databases, we have two approaches −
- Query-driven Approach
- Update-driven Approach
This is the traditional approach to integrate heterogeneous databases. This approach was used to build wrappers and integrators on top of multiple heterogeneous databases. These integrators are also known as mediators.
Process of Query-Driven Approach
- When a query is issued to a client side, a metadata dictionary translates the query into an appropriate form for individual heterogeneous sites involved.
- Now these queries are mapped and sent to the local query processor.
- The results from heterogeneous sites are integrated into a global answer set.
- Query-driven approach needs complex integration and filtering processes.
- This approach is very inefficient.
- It is very expensive for frequent queries.
- This approach is also very expensive for queries that require aggregations.
This is an alternative to the traditional approach. Today’s data warehouse systems follow update-driven approach rather than the traditional approach discussed earlier. In update-driven approach, the information from multiple heterogeneous sources are integrated in advance and are stored in a warehouse. This information is available for direct querying and analysis.
This approach has the following advantages −
- This approach provide high performance.
- The data is copied, processed, integrated, annotated, summarized and restructured in semantic data store in advance.
- Query processing does not require an interface to process data at local sources.
Functions of Data Warehouse Tools and Utilities
The following are the functions of data warehouse tools and utilities −
- Data Extraction− Involves gathering data from multiple heterogeneous sources.
- Data Cleaning− Involves finding and correcting the errors in data.
- Data Transformation− Involves converting the data from legacy format to warehouse format.
- Data Loading− Involves sorting, summarizing, consolidating, checking integrity, and building indices and partitions.
- Refreshing− Involves updating from data sources to warehouse.
What is Data Mining?
- Data mining is looking for hidden, valid, and potentially useful patterns in huge data sets. Data Mining is all about discovering unsuspected/ previously unknown relationships amongst the data.
- It is a multi-disciplinary skill that uses machine learning, statistics, AI and database technology.
- The insights derived via Data Mining can be used for marketing, fraud detection, and scientific discovery, etc
- Data mining is also called as Knowledge discovery, Knowledge extraction, data/pattern analysis, information harvesting, etc.
In this phase, business and data-mining goals are established.
- First, you need to understand business and client objectives. You need to define what your client wants (which many times even they do not know themselves)
- Take stock of the current data mining scenario. Factor in resources, assumption, constraints, and other significant factors into your assessment.
- Using business objectives and current scenario, define your data mining goals.
- A good data mining plan is very detailed and should be developed to accomplish both business and data mining goals.
In this phase, sanity check on data is performed to check whether its appropriate for the data mining goals.
- First, data is collected from multiple data sources available in the organization.
- These data sources may include multiple databases, flat filer or data cubes. There are issues like object matching and schema integration which can arise during Data Integration process. It is a quite complex and tricky process as data from various sources unlikely to match easily. For example, table A contains an entity named cust_no whereas another table B contains an entity named cust-id.
- Therefore, it is quite difficult to ensure that both of these given objects refer to the same value or not. Here, Metadata should be used to reduce errors in the data integration process.
- Next, the step is to search for properties of acquired data. A good way to explore the data is to answer the data mining questions (decided in business phase) using the query, reporting, and visualization tools.
- Based on the results of query, the data quality should be ascertained. Missing data if any should be acquired.
Benefits of Data Mining:
- Data mining technique helps companies to get knowledge-based information.
- Data mining helps organizations to make the profitable adjustments in operation and production.
- The data mining is a cost-effective and efficient solution compared to other statistical data applications.
- Data mining helps with the decision-making process.
- Facilitates automated prediction of trends and behaviors as well as automated discovery of hidden patterns.
- It can be implemented in new systems as well as existing platforms
- It is the speedy process which makes it easy for the users to analyze huge amount of data in less time.
Disadvantages of Data Mining
- There are chances of companies may sell useful information of their customers to other companies for money. For example, American Express has sold credit card purchases of their customers to the other companies.
- Many data mining analytics software is difficult to operate and requires advance training to work on.
- Different data mining tools work in different manners due to different algorithms employed in their design. Therefore, the selection of correct data mining tool is a very difficult task.
- The data mining techniques are not accurate, and so it can cause serious consequences in certain conditions.
♦Role and Uses of Technology Upgradation
Technology has allowed banks to offer much more to their customers like the facilities of card and telephone access, anytime and anywhere banking through 24hrs ATMs, credit card, debit card and POS (Point of sale) access. The technology has made it possible for the customers to have fingertip access to their accounts worldwide.
Data and Message Transferring
- Electronic Data Interchange (EDI): Banks have been using EDI in the form of SWIFT messages.
- Electronic Mail
- Dissemination of information
- Financial Advice
- To highlight non-banking activities
- A node for commerce
- Selling financial products
- Gateway to the Internet
- Account services
Management Information System (MIS): A management information system is an information system used for decision-making, and for the coordination, control, analysis, and visualization of information in an organization. The study of the management information systems testing people, processes and technology in an organizational context.
- Computer- based Information systems
- Decision Support Systems (DSS)
♦Impact of ‘IT’ on Banks
- Changes in Organisational Structure and Orientation
- Impact on Service Quality (Changes In Customer Aspirations)
- Impact on Human Resources (Role Transition, Training Needs)
- Impact on Privacy and Confidentiality of Data
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