Forrester defines big data as “the practices and technologies that close the gap between the data available and the ability to turn that data into business insights.”

Given the amount of investment in Big Data, Financial services have not been able to monetize and create any new insights. As a result, there are missed business opportunities and overlooked nefarious activities that are not only causing punitive damage but also hurting the reputation of the banks.

The goal of MLWiz is to address these challenges by adopting Machine Learning and Artificial Intelligence. Virtually every action in the financial institution results in some form of data. Whether it is retail bank account transaction or corporate deals, there is a chain of actions that takes place within the system. Each of these transactions are recorded and audited. However, most of the time the information represented is descriptive in nature and often overlooked.

The challenge: current monitoring and reporting solutions in the banks are operational in nature. They are unable to process All data and do not get a holistic view of the operating environment, while uncovering crucial insights and outcomes.

MLWiz solution understands the context of the problem and applies human intelligence, turning huge volumes of structured and unstructured data into valuable knowledge. Once the model is trained, it behaves like a human brain, it learns and improves with experience. Its accuracy and efficiency makes it feasible to monitor 100% of an organization’s data, making people more productive and leading to business outcomes that no one thought possible.


Despite significant investment in compliance to cope up with the newer and stringent regulations, by and large, financial institutions remain highly exposed.

The challenge: Compliance officers have started to realize that rules-based static monitoring tools can be easily circumvented and any attempts to increase the threshold often produce excessive false-positives. This results in higher cost and processing time due to detail investigation by compliance analysts.

MLWiz compliance solution takes a very different approach using Neural network and Deep Learning techniques to assess across a more comprehensive array of structured and unstructured data. Dots are connected and patters are detected to spot the problems sooner, turning compliance into a proactive function with high degree of precision covering into significant business values.


Business leaders understand that improved customer insights lead to better and informed opportunities that maintain and grow profitable relationships. This is especially true within financial institutions where relationships, conversations and opportunities are not always obvious.

MLWiz delivers transparency. By monitoring electronic communications and contextually analyzing who’s talking to whom, and what they’re talking about, MLWiz helps client relationship managers have a complete view into their customer.  Whether there is a cross-sell opportunity or a client is dissatisfied, MLWiz knows and alerts the business to take action. The result; business grows, problems are averted, and satisfaction rises.


Health care professionals rely on insights from data to make difficult decisions and ensure they can act fast in live situations. The health care industry faces tough data challenges that can make it difficult to deliver those insights with sufficient quality and timeliness.

The complexity of medical diagnoses and chains of communication within and between health care providers can easily produce knowledge and awareness gaps. Adding to the problem is fragmented storage of records and the inability of legacy systems to extract meaning from medical notes written in natural human language.

MLWiz helps health care professionals achieve the best outcomes. It uses Machine learning algorithm that gathers, reads and understands medical data whatever its format or location and analyzes it as a whole. Knowledge gaps are filled, problems are detected sooner, and unexpected insights are brought to light.

Health care professionals take immense care to document patients’ medical treatment and health condition. Electronic health records (EHRs) provide a vital chain of reference that aids diagnoses, decisions about future care requirements, and coding for insurance payments. EHRs also provide a collective knowledge that gives early insight into emerging problems, such as hospital acquired infections.

As medical records are often highly fragmented between systems and locations and contain complex narratives and diagnoses, it’s difficult for traditional analytics solutions to detect valuable insights that can aid in patient care and improve operational efficiency. A definitive holistic picture requires piecing together a complex data jigsaw, which is not always practical.


Digital images and videos are the most valuable assets of Media companies. MLWiz is harnessing Deep Learning technology and Neural Networks to automate the processes and improve the search and indexing capability.