Gegevens Mining te Banks and Financial Institutions, Rightpoint
With the lattest news displaying clients of large banks fleeing to smaller credit unions and local banks and spil banking competition becomes more and more global and intense, banks have to fight more creatively and proactively to build up or even maintain market shares. Gegevens mining is becoming strategically significant area for many business organizations including banking sector. It is a process of analyzing the gegevens from various perspectives and summarizing it into valuable information. Gegevens mining assists the banks to look for hidden pattern ter a group and detect unknown relationship te the gegevens.
Banks which still rely on reactive customer service technologies and conventional mass marketing are fated to failure or atrophy. The banks of the future will use one asset, skill and not financial resources, spil their leverage for survival and excellence. Remarkably, most of this skill are presently te the banking system and generated by daily transactions and operations. This valuable information need not be gathered by intrusive customer surveys or expensive market research programs. The only problem is that this storehouse of gegevens has to be mined for useful information.
Normally unmined and unappreciated, thesis terabytes of transaction gegevens are collected, generated, printed, stored, only to be filed and discarded after they have served their short-lived purposes spil audit trails and paper trails. Most gegevens generated by the canap’s information systems, manual or automated like ATM’s and credit card processing, were designed to support or track transactions, sate internal and outer audit requirements, and meet government or central handelsbank regulations. Few are gathered intentionally and originally to generate useful management reports.
Current information systems are not designed spil decision support systems (DSS) that would help management make effective decisions to manage resources, rival successfully, and enhance customer satisfaction and service.
Consequently, adhoc or even the most basic management reports have to be extracted excruciatingly from scattered and autonomous gegevens centers or islands of automation that use incompatible formats. The results are management reports that are perennially late, inaccurate, and incomplete. Executive decisions based on thesis misleading reports can lead to millions of dollars ter brief and long term losses and lost opportunities and markets.
The tremendous increase te the power of information technology will enable banks to tapkast existing information systems, also known spil legacy systems, and mine useful management information and insights from the gegevens stored te them. This process can be done without the need to switch the current systems and the gegevens they generate. But before gegevens mining can proceed, a gegevens warehouse will have to be created very first. Gegevens warehousing is the process of extracting, cleaning, converting, and standardizing incompatible gegevens from the bankgebouw’s current systems so that thesis gegevens can be mined and analyzed for useful patterns, relationships, and associations.
The gegevens warehouse need not be updated spil regularly or daily spil the transaction based systems. Gegevens warehouses can be updated and mined spil infrequently spil the need for management reports and decisions dictate, i.e., monthly, quarterly, or on a ad hoc voet. Gegevens warehousing and mining can run parallel with banking transaction information systems, without intrusion and interruptions.
What are the benefits and application of gegevens mining ter the banking industry? One of the earliest application of gegevens mining wasgoed ter retail supermarket. Mining the volumes of point of sale (POS) gegevens generated daily by specie registers, the store management analyzed the housewife’s shopping basket, and discovered which items were often bought together. This skill led to switches ter store layout the brought the related items physically closer and better promotions that packaged and sold the related items together. The skill discovered also led to better stocking and inventory management. Retailers like WalMart have experienced sales increase spil much spil 20% after extensively applying gegevens mining. Some frequently bought voorwerp pairs discovered by gegevens mining may be demonstrable, like toothbrush and toothpaste, wine and cheese, chips and spuitwater. Some were unexpected and bizarre like disposable diapers and mannetjesvarken on Friday nights.
Ter banking, the questions gegevens mining can possibly reaction are:
1. What transactions does a customer do before shifting to a competitor canap? (to prevent attrition)
Two. What is the profile of an ATM customer and what type of products is he likely to buy? (to cross sell)
Trio. Which handelsbank products are often availed of together by which groups of customers? (to cross sell and do target marketing)
Four. What patterns ter credit transactions lead to fraud? (to detect and deter fraud)
Five. What is the profile of a high-risk borrower? (to prevent defaults, bad loans, and improve screening)
6. What services and benefits would current customers likely desire? (To increase loyalty and customer retention)
Note that gegevens mining does not commence with a hypothesis that has to be proven or disproven. It is an exploratory process aimed at “skill discovery” rather than the traditional “skill verification”. Skill verification DSS otherwise known spil OLAP (on line analytical processing) would ask straighforward questions like “how many card holders defaulted this month compared to the same month last year?” or “how many of our ATM customers are also borrowers?” While OLAP queries are useful, they are not spil insightful, powerful, and spil focused spil gegevens mining queries, especially te preempting competition or preventing customer attrition.
The gegevens miner does not have a priori skill or assumptions. The gegevens mining software will usually expose unexpected patterns and opportunities and make its own hypothesis. Gegevens mining will be the cornerstone of the competitive if not the survival strategy for the next millennium ter banking. Banks which disregard it are providing away their future to competitors which today are busy mining.