blog post data

Data Mining in the Financial Industry


, No hay comentarios

The financial environment is a highly competitive area in which information is a very valuable component. Rather than simply focusing on data processing, it is becoming more and more indispensable to rely on innovation as well as innovative processes. By becoming oversaturated with excess data, finding that one bit of relevant information containing added value often becomes quite a challenge in itself. Currently, amidst the expansion of the Information Society, the ability to transform excess data into relevant information is essential. An appropriate analysis is required to properly adapt to market necessities. Due to the abundant information created on a daily basis, the capacity to extract information that can be of interest becomes vital. www.,  is a clear example of solutions based on this concept, integrating consumer relationship modules, business intelligence, portfolio quantity analysis, transactions and all types of information relevant to a more agile process through automation. Extracting Bank Data In banking, information regarding transactions, market assets, client portfolios, operations, profitability, behaviour patterns and client acquisitions is gathered and stored in databases within each entity’s reach. Combined with information on market behaviour along with the elements which take part in its composure, banks have access to complete information which allows them to predict the way their clients will act in the future and design optimal strategies accordingly for each segment. Extracting relevant data, or Data Mining, filters and analyzes all of the available information with the objective of reaching valuable conclusions. These conclusions are formed based correlation and pattern detection, along with other significant relations. Moreover, it enables human error isolation, a factor in play which often becomes a risk without the necessary technology and software. Efficiency and detail orientation throughout Data Mining processes allow for the complete stored and available data to be analyzed all the while reducing the error margin and the time invested in said process. By digitalizing the data extraction process also enables these institutions to avoid wasting resources and energy on expensive tasks which demand a great amount of time investment. At the same time, this does not imply that human intervention becomes exempt. On the contrary, Data Mining lacks personnel to finalize the process in terms of decision-making. Having this privileged information at their disposal along with the corresponding conclusions they allow, ease the entire decision-making process, allowing for precision, certainty and conviction every time. The Data Mining Process Within the Data Mining process, a series of integrated phases exist with specific objectives which contribute to reaching greater data comprehension and filtering.
  • Selection, pre-processing and data transformation As its title may indicate, this phase consists of recollecting the combined stored information within the existing databases. By including all of the available information, the first part of processing applies an initial filter which eliminates inconsistencies and irrelevant information in order to exclusively consider significant data.
  • Core Data Mining and Conclusions In the centre of this process lies data analysis. Information is evaluated to detect patterns and value their relevance. Some techniques include grouping and association. In the first case, the analytical process groups together data which is in itself similar, or represents substantial behavioural similarities. Through the detection of patterns and analyzing data history, a prevision technique becomes a viable way to predict future behavioural patterns by establishing a reference in the correlation between dependent and independent variables, along with their tendencies.
  • Applying Data Mining results One of the main advantages of the adequate interpretation of data is reaching a more complete level of client comprehension. Within the financial sector, being able to access this information, along with the capability to understand these clients, makes it possible to offer an improved service adapted to their specific necessities. In this sense, the bank at hand can also reinforce their values, increasing their capacity to attract and retain their clients.
Another frequent application of Data Mining is detecting suspicious activity related to fraud among the financial sector. Properly treating and interpreting this information offers an unlimited amount of possibilities, all of which are a big part of how the field’s functional methods are evolving. That being said, the data processing method is proving to transform into one of any financial institution’s most valuable assets. My T-Advisor for years now we aim for innovation and in all of the tools available for independent investors. For this reason, for the past months, the Innovation department has been working on providing solutions that bring a large amount of added value to financial entities through analysis and data treatment that reside within our solutions, lifting them up to a superior level. We provide market data, as well as data regarding clients’, trends, portfolio models, client profile ranges and all the bits of information specifically pertaining to day to day portfolio management.

Se el primero en escribir un comentario.

Escribir un comentario: