When it comes to adopting NLP products, companies are often deterred by the complexities of implementing and training such a technology. Without the skills of expert coders and technicians, firms may feel overwhelmed and underprepared for such a feat, conscious of time and resources needed to build verses buy.  

Making sense of human language, a trainable Natural Language Processing (NLP) solution holds considerable benefits for a firm, utilising key data insights taken from trade communications. With the vast quantity of unstructured data recorded and stored daily, firms need a solution which can not only optimise the use of this data but be easily updated through model training.  

How to adopt NLP to increase front office competitivity 

With traditional computing technologies predicted to hit a wall in the coming years according to Gardiner’s Top Predictions for IT Organizations and Users in 2021 and Beyond, the demand for neuromorphic computing, such as NLP, is increasing at an encouraging rate. Despite what may seem daunting terminology, firms who adopt this form of AI at an early stage are better placed in an ever-more competitive market.  

Concerns around exposing trade secrets or winning sales strategies has led to firms opting to build NLP solutions from scratch however this can take a lot of time and money to develop to a good standard. Solutions are now available that offer flexible training capabilities so clients can keep ownership of their data while they benefit from the expertise of a vendor solution and customise it in order to enhance data return. Firms can then create their own inputs, tailored to their specific needs enabling businesses to visualise insights from their data and capitalize on trade opportunities. 

Here are three considerations when using NLP to stay competitive in the market: 

  1. Take advantage of pre-trained models for your sector and business area

In order to use NLP to gain a competitive edge, the technology must be trained to recognise important, sector-specific terminology, with a high degree of accuracy. This is very hard for a company to do from scratch. Not only does it require expert personnel to train NLP models, the process can take years to build up a sufficient amount of training data that yields accurate results.  

A pre-trained NLP model can greatly accelerate the tuning process and enables firms to build on an already accurate model and customise it how they see most profitable for their business. Models which apply Deep Learning can learn patterns in communications from much fewer inputs starts to recognise similar data points which are relevant to each specific firm, the more data it processes (without any additional training). This process therefore requires less data, less time, less manual input and less financial resources to train the model. 

  1. Train models regularly in-house to stay in control of your insights

As previously mentioned, the issue of data ownership has deterred firms from outsourcing their data surveillance and analytics needs. Opting for an on-premise installation of technology such as AI modelling and analytics can increase a firm’s control when it comes to training and tuning.  

While pre-programmed modules are preferential as a baseline, once deployed, a firm are encouraged to train the model regularly in a manner that best suits business needs – increasing competitivity and revenue opportunity.  

This approach not only provides tailored data insights, but also has the power to directly increase competitivity in a very short period of time. With little technical knowledge and access to user-friendly training environment, firms can train models with plain text and tags to capture trade opportunities accurately. Furthermore, trade managers can easily identify and flag key market makers, with the security of not revealing trade positions via a third party.  

  1. Consider cost when it comes to buying vs. building NLP.

As discussed in our previous blog, when it comes to selecting the best option for your business, cost plays a vital role in this decision. When building a solution in-house cost can quickly stack up, with such a task stretching many months. Experts are needed and often come at a considerable cost, with unforeseen issues arising that may add additional expense. This is even before firms have the opportunity to train these models in house.  

When purchasing from an expert vendor often firms see a higher return on investment with quick implementation and pre-programmed models, that can be trained with ease. Costs are set out from the initial stages of the order, with no nasty surprises along the way!  

 

With the increasing competition within capital markets firms must differentiate their offering by embracing AI. Doing so with VoxSmart’s trainable NLP solution, tailor made to understand financial jargon and delivered on prem just makes sense! 

Contact VoxSmart to request a demo here! 

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