IBS Journal July 2018
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factor here is the ability to drive an automated ingestion and accurate mapping of data from source systems into a central repository.
Continuous discovery: building effective ML models
As ML platforms tend to become more sophisticated, the model efficacy becomes a function of continuous improvement to the rules engine and the ML models. Determining misconduct in a trading activity, for example, is a continuous process of corelating discrete Natural Language Processing (NLP)-enabled data across telephonic calls and emails with the underlying trade. NLP allows for machines to decode human language, both written and spoken. Agile approaches allow for multiple rounds of testing to fine-tune results and improve the workflow, in what one would call as a continuous discovery process, and this forms the bedrock of any effective ML environment. The key here is to minimise external intervention, with a self-learning approach, using what is known to be the ‘Model Sandbox’. New ML models tend to offer a deeper and insightful view of the data, than what was previously possible.
Real-time execution: integrated architecture
The core value proposition of any ML tool is in the real-time processing and execution of the model. A case in point is when millions of cross- border transactions are processed for fraud detections, reducing the manual interventions in anti-money laundering validations. Determining that there is a potential fraud would be of no value, should the corrective action not be executed in real time. And this involves having an application architecture that is tightly integrated with the ML tools.
An extended example can be made about the use of ML in cyber security executing all the above three steps. Sifting reams of log-scans
(data model) and determining hostile threats that need to be early- warned (ML model) and executing an immediate act to self-protect the system from a data loss (real-time execution) are all carried out in real time for true impact. The concept of machine-to-machine exchange of information, driven by the Internet-of-Things (IoT) can also deliver its true potential only from an architecture that enables real-time execution.
While the examples discussed above are mostly built around financial fraud detection and innovation in regulatory solutions, the concept extends well across other domains, too. For instance, a well-developed ML platform could effectively conduct screening tests and background checks to validate if the applicant for a HR interview is indeed the person that he/she is claiming to be. Legal firms are looking to use ML to read and review contracts to identify risks embedded in them. With the emergence of machine readable regulations, we could expect to see a much lower error rate in interpretation that is presently resulting from ambiguity in understanding.
Are there areas to be extra sensitive here? Obviously, there is always another side to the coin. The sophistication of data analytics and ML also comes with the responsibility of more vigilant data security and governance. Investing in the right information architecture and improvising on it also becomes pertinent as sources of data evolve, and more importantly when machines have begun to learn how to get them effectively applied.
At the end of the day, the question still remains: can machines learn better than humans? The jury is still out there, but it would pay to watch some emerging examples – behavioural biometrics is one of them. Machines can now go well beyond signature verification, into recognising voice patterns, accents and even the distance to the mike based on the customer’s holding position of a phone. For sure, the days to come are likely to be more interesting than before.
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