Over the last 3 years we’ve seen many firms in the financial sector scrambling to meet the compliance requirements of multiple regulations that have been introduced.  The revised Markets in Financial Instruments Directive known as MiFID II went live on January 3 this year following the 2016 publication of the FCA’s Market Abuse Regulation, and the EU’s GDPR enforcement deadline May 25 is just around the corner.

These are just three of the new regulations entering the financial sector and between them they contain significant and wide-spread legislative reform.  Multiple aspects are covered, from trading to recording and monitoring of data, transparency around research costs, new product governance rules and increased personal data protection.  These regulations also carry the potential for heavier fines for non-compliance.  GDPR is an example of this with maximum fines of up to 4% of annual global turnover or €20 Million (£17 Million) for serious breaches compared to the £500,000 maximum applied by the ICO.

As regulations in the financial sector continue to grow in number and complexity, companies need to continue substantial investment to meet their obligations.  For many organisations, the required increase in compliance initiatives, systems and process changes will become unsustainable as staffing commitments continue to grow.  In some institutions this strain is already felt with compliance staffing numbers now matching front office staff one-to-one.

We believe that the increase of regulations can provide many opportunities for organisations to see a return on investment from these ‘enforced’ initiatives if they take a forward-thinking approach.  We’ve implemented many different solutions for businesses to meet their compliance obligations and the most rewarding are those that use the data they’re collecting.  Companies collect and store huge amounts of data and this will continue to increase as more regulations are introduced.  Organisations should be making use of innovative technology to mine this data to achieve significant business insight which can be applied to operating and growth strategies.


One of the requirements being enforced by many regulations is the collection and storage of communication information which must be readily available at the request of authorities or clients.  To ensure regulation requirements are met, actively monitoring this communication information is important for the early detection of possible compliance breaches.  However, the struggle for many companies is in identifying how to successfully transform this communication information into data that makes sense.  Analysis of this data in context to business events and incidents can provide powerful insights which can be applied to important business decisions.  The key to unlocking this potential value lies in the use of NLP (natural language processing) and Machine Learning.  With the use of NLP and Machine Learning these ‘soon-to-be’ terabytes of communication can be monitored and analysed to provide near real-time insights, not to mention exceeding any expectations the FCA or investors would have.

For example, Lloyd’s Banking Group employs NLP in conjunction with ML techniques to identify fraudulent phone calls and Deutsche bank has shown that NLP-based techniques can provide significant improvement to quantitative investing models and stock price prediction.  Amazon has also employed NLP to huge success with their Alexa-enabled devices. According to industry estimates sold approximately 11 million Alexa-enabled devices by the end of 2016, or roughly 70 percent of the existing market at the time for virtual assistant products.

Further-more, NLP-based sentiment analysis techniques can be applied to stored communications records, reports, and even social media or other web content to effectively determine whether those sources contain positive or negative expressions.  We have been working with organisations who record and store their communication information to provide them with real-time monitoring and analysis of this data through the use of NLP and Machine Learning.  This not only ensures companies remain compliant with early detection alerts of potential breaches, but results in further benefits such as the deep understanding and monitoring of what their clients and associates think about them, their products and services.  Analysed in the context of business as usual, it could assist in pre-empting margin calls for example, this is extremely powerful.

Hiroshi Sasaki, our Head of NLP at Synetec, is highly experienced in building the knowledge bases behind NLP utilizing active learning, text mining and LUI (language user interface), he says,

“Using NLP can turn your compliance costs into a business opportunity by giving you significant business insights. Through readily available and improved transcription technologies, NLP and machine learning we can accurately identify topics, behaviour and the related sentiment. Who said what, how they said it and where they said it!”

Hiroshi completed a Master’s Degree in Computational Linguistics at the Nara Institute of Science and Technology in 2003 and has previously worked for Toshiba in their Research and Development Centre in Japan.  Hiroshi’s projects focussed on how to reduce the cost of manual data labelling that is required for ML and he is very interested in effectively applying these techniques to real business problems.

There are several components that need to be understood to truly make NLP work for you and your business, including Topic, Behaviour and Sentiment Identification.  These areas provide huge benefits in the use of NLP techniques to mine your data effectively and we’ll be writing about each of these in future posts, so stay tuned.


Synetec is a solutions provider certified in many diverse development technologies, such as Microsoft and AWS, delivering integration and development solutions since 2000.

We work with a number of the UK’s most respected financial institutions to deliver a range of innovative solutions. We have expertise in working with both established businesses as well as start-ups and extreme growth businesses.


Written by Natasha Walters

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