#PCSEx

Company Profile

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About Gulf Business Machines (GBM): Empowering businesses for over 33+ years, Gulf Business Machines (GBM) stands as a leading end-to-end digital solutions provider in the region. With a widespread presence, encompassing 7 offices and a dedicated team of over 1500 professionals, GBM offers a comprehensive portfolio, including cutting-edge digital infrastructure, business solutions, security, and services.

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We have nurtured partnerships with the world’s leading technology companies and invested in a talented, skilled workforce to implement solutions that cater to customer’s specific, complex and diverse business needs. With on-the ground, readily available expertise, GBM provides round-the-clock support to customers across the region.

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GBM Pakistan was founded in 2007, as part of the company’s strategic expansion plan to meet the growing demand in the region. GBM Pakistan operates out of three cities throughout the country; Karachi, Lahore, and Islamabad.

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GBM Pakistan works across a number of industry sectors, including banking and finance, telecommunication, manufacturing, and a growing number of SMBs.

Product

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IBM Safer Payments

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IBM Safer Payments is the industry's first true cognitive fraud prevention solution to payment processing and protects some of the largest and most complex payment portfolios in the world. Users report that IBM Safer Payments significantly reduces fraud losses while keeping false alarms to a minimum. This new approach services all payment channels. 

How IBM Safer Payments is different 

First generation payment fraud prevention solutions use coded expert experience. This included velocity counters and expert rules to identify high risk. The value of this approach was in its simplicity. However, the ever-increasing number and complexity of fraud patterns have rendered this approach inefficient. 

Second generation solutions generated fraud detection models from past data. Neural networks and advanced statistics used for this typically required collecting large amounts of data over a long period of time. This data is then sent to the vendor that creates a model off site. By the time the model is put into production, the fraud patterns it detects can be a few years old. 

Second generation solutions date back from a time when fraud patterns only changed slowly, so this was not too significant a problem. In today's world, however, new patterns of fraud attack are frequently introduced and the speed of change is only accelerating. As a result, neural network vendors have thus added first-generation rules engines to their products, so that the customers can create workarounds to their aging models. This is because neural networks are black boxes; they only generated a score that cannot be explained by looking at the model. In addition, the neural network model cannot be explicitly modified. Experience shows that some users of neural network-based solutions have moved away from dependence on the model and only use the rules function to mitigate cost and erratic performance. 

IBM believes that using one modelling technique to fix the shortcomings of another is a logically flawed approach. To avoid this flawed approach, next generation solutions use cognitive computing to create one clean and efficient model. 

Next generation IBM Safer Payments' cognitive approach also uses automatic learning from past data. But rather than generating a black box model, it generates easily readable rules and fraud prevention scenarios, IBM Safer Payments enables a generation of new or revised models with considerably less data and renders faster model update cycles, helping to result in higher fraud detection rates at drastically lower false positive rates. 

In addition, local specialists can enhance such rules and scenarios as they are easy to read and to understand. They can also combine them with their own human experience, and perform frequent updates of the models. In fact, the automated learning is so fast and efficient that some users elect to adapt their models up to multiple times per week.