Banking services (lending and borrowing) that occur between other financial institutions and merchant banks refers to as wholesale banking. This provision of services offered by banks towards larger customers or organizations like international trade finance businesses, large corporate clients and mortgage brokers, to name a few. The services include large trade transactions, currency conversion and working capital financing along with others of the similar kinds. Wholesale banking includes extremely large sums of money.
What is Data Mining? Data mining is a process that facilitates the extraction of relevant information from a vast dataset. The process helps to discover a new, accurate and useful pattern in the data to derive helpful pattern in data and relevant information from the dataset for organization or individual who requires it. Key Features of data mining include: Based on the trend and behaviour analysis, data mining helps to predict pattern automatically. Predicts the possible outcome. Helps to create decision-oriented information. Focuses on large datasets and databases for analysis. Clustering based on findings and a visually documented group of facts that were earlier hidden. How does data mining work? The first step of the data mining process includes the collection of data and loading it into the data warehouse. In the next step, the data is stored and managed on cloud or in-house servers. Business analyst, data miners, IT professionals or the management team then extracts these data from the sources and accordingly access and determine the way they want to organize the data. The application software performs data sorting based on user’s result. In the last step, the user presents the data in the presentable format, which could be in the form of a graph or table. Image Source: © Kalkine Group 2020 What is the process of data mining? Multiple processes are involved in the implementation of data mining before mining happens. These processes include: Business Research: Before we begin the process of data mining, we must have a complete understanding of the business problem, business objectives, the resources available plus the existing scenario to meet these requirements. Having a fair knowledge of these topics would help to create a detailed data mining plan that meets the goals set up by the business. Data Quality Checks: Once we have all the data collected, we must check the data so that there are no blockages in the data integration process. The quality assurance helps to detect any core irregularities in the data like missing data interpolation. Data Cleaning: A vital process, data cleaning costumes a considerable amount of time in the selection, formatting, and anonymization of data. Data Transformation: Once data cleaning completes, the next process involves data transformation. It comprises of five stages comprising, data smoothing, data summary, data generalization, data normalization and data attribute construction. Data Modelling: In this process, several mathematical models are implemented in the dataset. What are the techniques of data mining? Association: Association (or the relation technique) is the most used data mining technique. In this technique, the transaction and the relationship between the items are used to discover a pattern. Association is used for market basket analysis which is done to identify all those products which customer buy together. An example of this is a department store, where we find those goods close to each other, which the customers generally buy together, like bread, butter, jam, eggs. Clustering: Clustering technique involves the creation of a meaningful object with common characteristics. An example of this is the placement of books in the library in a way that a similar category of books is there on the same shelf. Classification: As the name suggests, the classification technique helps the user to classify and variable in the dataset into pre-defined groups and classes. It uses linear programming, statistics, decision tree and artificial neural networks. Through the classification technique, we can develop software that can be modelled so that data can be classified into different classes. Prediction: Prediction techniques help to identify the dependent and the independent variables. Based on the past sales data, a business can use this technique to identify how the business would do in the future. It can help the user to determine whether the business would make a profit or not. Sequential Pattern: In this technique, the transaction data is used and though this data, the user identifies similar trends, pattern, and events over a period. An example is the historical sales data which a department store pulls out to identify the items in the store which customer purchases together at different times of the year. Applications of data mining Data mining techniques find their applications across a broad range of industries. Some of the applications are listed below: Healthcare Education Customer Relationship Management Manufacturing Market Basket Analysis Finance and Banking Insurance Fraud Detection Monitoring Pattern Classification Data Mining Tools Data mining aims to find out the hidden, valid and all possible patterns in a large dataset. In this process, there are several tools available in the market that helps in data mining. Below is a list of ten of the most widely used data mining tools: SAS Data mining Teradata R-Programing Board Dundas Inetsoft H3O Qlik RapidMiner Oracle BI
Earmarking is a term used in the banking industry to refer to funds, which has been set aside to pay for some specific projects. The term is usually associated with transactions as well as earmarked transactions, which could be defined as the business transaction, made to claim any pre-allocated fund to meet the expenditure.
Every company operates with limited resources to perform various business activities. Under this framework, it is possible that a company receives bulk orders from customers. In this situation, the companies ask a wholesaler or a supplier for additional stock to satisfy customers’ needs. These orders are termed as backorder.
What is backward integration? Backward integration is a form of vertical integration which involves companies acquiring or creating processes enabling the company to produce its own inputs. These processes are those which the company had previously assigned to other companies up the supply chain. Complete vertical integration is achieved when a company is involved in all the stages of the production process. This can be achieved by the firm either by mergers and acquisitions with the companies in the supply chain, or by starting its own subsidiary to perform the tasks which had formerly been assigned to these companies. Copyright © 2021 Kalkine Media Pty Ltd Backward integration is a form of vertical integration used to make the business more competitive. What are some examples of vertical integration? Vertical integration can be better understood with an example. Consider an oil refining company like Marathon Petroleum Corporation that depends on another firm for providing it with crude oil. Marathon refinery purchases raw oil from other oil exploration companies and is only engaged in refining the oil and selling it. However, if Marathon were to acquire or merge with these oil exploration companies then it would be called a backward integration for Marathon. Consider a restaurant engaged in the production of wheat and potato-based products. The firm usually acquires these products through long-term contracts with farmers or wholesale grocery suppliers. If the firm now decided to start its own plantation growing wheat and potatoes, then it would be a backward integration. Here the firm has chosen to create its own production process without having to merge with or acquire any other company. This gives the firm endless possibilities to change how these inputs are processed. For instance, the firm can choose to opt for organic farming, which it could then advertise to its customers. How is backward integration different from forward integration? A company’s supply chain refers to the different stages involved in achieving the final good. The processes lying upwards in the chain are the initial stages while the processes lying further down are the final stages including the sale of the product or service. Backward integration involves integrating those production processes into the company’s operation that lie on the upper side of the supply chain. Whereas forward integration involves the integration of those processes into the firm’s operation that lie on the lower end of the supply chain. Forward integration involves companies acquiring or merging with those firms that are engaged in the distribution or in the retailing process of the product. For example, consider a cheese processing company that sells its product to a retailer for resale. If the company decides to set up its own retail chain, or its own digital platform to sell its cheese then it would be forward integration. What are the advantages of backward integration? Higher control: Integrating upper-level supply chain processes into a firm’s operation gives it higher level of control over how the final good turns out. This also allows companies to conduct their supply chain management more efficiently. Thus, there can be higher level of differentiation in the final goods as compared to other competitors. Additionally, the company would have a fixed supply of input when the subsidiary is engaged in raw material production. Competitive Advantage and Barriers to Entry: Acquiring a company through backward integration can allow access to exclusivity of the supplier. Other companies may no longer approach the supplier once the firm acquires or merges with it. This adds a competitive advantage to the firm and creates barriers to entry for other companies. Cost Cutting: When a raw material producer supplies it to companies lying lower on the supply chain, it would charge a mark-up over the actual cost of production to gain profits. However, if a firm were to become its own input supplier, then there would be no mark-up costs involved for it as it is producing for itself. What are the challenges with backward integration? Copyright © 2021 Kalkine Media Pty Ltd Lack of competitiveness: Removing competition by acquiring the supplier could sometimes have more adverse effects than benefits. Reduced competition could make a firm less competitive and hence less efficient. This could reduce the innovation in the firm and cause it to produce poorer quality products. Financial requirement: To acquire with a full-fledged supplier, firms must have adequate capital. Thus, backward integration can be a huge investment for firms. Many companies may consider debt financing which could end up hurting their balance sheet.