TREELOGIC TEAM| 15/02/2019
The ability to detect fraudulent situations is a delicate subject that applies to many industries, especially affecting companies in the financial and insurance sectors.
In recent years we have seen an upward trend in the number of attempts at fraud, causing serious economic damage to the companies targeted. While the affected institutions have been improving their security in response to this increase in cases, it is difficult to detect the problem because these incidents represent a small percentage of the total.
The use of technology based on mining data and information gathered from different sources helps to analyse, anticipate and quickly detect fraudulent behaviour in order to take effective measures and try to avoid or at least minimise losses. Through the use of sophisticated technological tools that extract data from both external and internal sources, millions of movements can be observed to detect clear patterns of behaviour and potentially fraudulent situations.
The first step in detecting fraud is to identify the factors that give rise to the deception. Knowing what phenomena or patterns repeatedly occur before, during or after fraud and analysing them in depth will provide the ability to predict and detect these fraudulent situations.
Thanks to advanced information processing technology, deep and automated learning, decision trees and an endless number of complex computer systems, predictive models can be generated to understand the probability of a fraud. This early warning system helps to better optimise resources and avoid large losses.
Treelogic has been working closely with banking organizations for many years and this has allowed us to gain experience in the sector in order to offer high quality solutions to fraud-related problems.
In banking, and in the financial sector in general, fraud is a common problem which most companies have to deal with on a daily basis. There are many examples of scams related to the fraudulent use of banking products and services and anticipating their existence can save large amounts of resources.
For banking companies, the analysis of their massive amount of data is of vital importance. Obtaining, classifying and processing this information from users will make it possible to create predictive models of the specific behaviour of their customers, knowing what type of risks are assumed with a given operation. With that information, organizations can make the best decisions for the well-being of their business.
The Big Data solutions offered by Treelogic resolve issues related to fraudulent behaviour. We offer advanced and innovative technologies that analyse in real time the data of thousands of customers to detect potentially malicious behaviour. The Treelogic methodology always aims to add value to our collaborating companies. To achieve this goal, our team of professionals with years of experience in the sector uses different tools developed by Treelogic's research and development department.
However, the technology that we provide to our customers is not only helpful in detecting fraudulent behaviour, it is also very useful to meet the needs of new customers, with quickly changing habits and adapted to new times. In this way, business opportunities are exploited in a more efficient way.
Another type of company highly affected by fraud is insurance companies, being one of the sectors with the highest percentage of fraud cases. Detecting these behaviours in time is essential for organisations in the insurance sector to survive.
One of the major drawbacks in the fight against this type of deception is the difficulty in uncovering specific cases of fraud. At Treelogic we know that this is a problem and we offer technological solutions for the detection and analysis of fraud in the insurance sector. We have advanced systems, based on Big Data and Data Science, which offer us the opportunity to process a large amount of information to detect the misuse of insurance company services.
Thanks to the experience acquired through working in the sector for many years, we know first-hand the needs of the market and the main problems that arise. With this know-how, our development department has been able to create complex tools that optimize resources and speed up decision-making.
SERIF stands for a set of technological solutions developed by Treelogic focused on solving insurance fraud problems. A fundamental factor in avoiding problems of this type is the analysis of digital files.
Among the tools Treelogic offers to combat fraud is one that detects alterations of any kind in documents, images or any other files. Its technology allows it to detect possible modifications to the content for fraudulent purposes, which makes it possible to anticipate and intervene to avoid major problems.
Another application of the SERIF set of solutions is the use of Big Data to analyse information from external sources in order to combine it with existing information. The synergy between internal and historical data used in conjunction with external and immediate data makes it possible to focus efforts on detecting possible cases of deception using multiple information sources.
And as with all Treelogic projects, our team of specialists proposes a methodology for working with the client from start to finish. So at each stage of work there is always communication between the two parties, thus completing all phases of the project
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