Utilization of A.I. for Commerce & Financial Industry

Posted by Gathenya J. M. on Sep 22, 2017 4:37:34 PM


The world is undergoing a technological revolution that impacts practically every aspect of business and personal life. The computing power has advanced greatly driven by new chip designs and fast-improving designs. Additionally, the era of Big Data is with us as organizations generate voluminous gold mine of structured and unstructured data at very high speed from many different sources and in many formats. At the heart of today’s technological revolution is Artificial Intelligence (AI).

AI refers to a branch of computer science that is dedicated towards development of computer solutions which mimic intelligent human behavior. In its application, artificial intelligence refers to the application and development of smart computers and software which solve real-world — and typically business-related — problems.

Do you have tasks which are repetitive, follow a regular pattern, have high volumes, require limited judgment, and have a low cost for mistakes? Such tasks are potential candidates for artificial intelligence implementation.

In business circles, AI has three main techniques which can directly impact the way your organization does business. Here is AI main areas and their application in commerce & financial industry:

  1. Connecting Relations of Information
    AI is used to develop software applications which utilize a series of routines seeking to identify underlying relationships in a set of data by using a process similar to the way the human brain operates. This branch of AI is called Neural Networks with application areas in business includes;
  • Target marketing:
    Profile your customer database and segment them according to their customer behavior. Through this segmentation, you are able to develop custom products and marketing tools for each category of customers in your database.
  • Sales Forecasting:
    Compared to other traditional statistical techniques such as regression analysis, neural networks are a perfect solution for sales forecasting since they are able to simultaneously consider multiple parameters such as customer’s disposable income, market demand for a product, population size, product price and price of complementary or substitute products.
  • Shopping cart analysis:
    A great alternative for analyzing shopping cart data that is able to establish critical trends in retail shopping. For example, using neural networks, it’s easy to establish which products are often purchased together and such information would be helpful in making decisions on store layout.
  • Application in the financial sector:
    Analyze large volumes of financial data in order to make credit scoring systems and bankruptcy prediction applications in the financial services sector.


  1. Trend Prediction
    Another application of AI in business is in the development of computer programs that have the ability to learn from a fixed set of data and make predictions based on that data. This branch of AI is called machine learning and its applications include:
  • Recommendation system:
    These are information filtering applications which provide details of products that are likely to be of interest to customers. They are in great use by e-commerce and Video-On-Demand (VOD) platforms that use collaborative filtering. Companies such as YouTube, Amazon, and eBay make great use of recommendation systems.
  • Online fraud detection:
    Machine learning is widely used in the development of automated credit card fraud screening systems that can help businesses in reducing fraud.
  • Financial Trading:
    When supplied with market data, machine language applications have been used to predict the stock prices with a great level of accuracy.

    At WGS, we have highly-qualified, competent staff in using Amazon Machine Learning to develop predictive models fraud detection, content personalization and much more.


  1. Developing Solutions
    This refers to the application of neural networks to develop learning solutions for tasks which have multiple hidden layers. These techniques are used in processing huge volumes of data and called Deep Learning, with business applications include;
  • Image Recognition:
    Automatically search and recognize a collection of photos with no identifying tags. This has been in use by the likes of Google and Amazon. Additionally, image recognition using deep learning has seen wide usage in improving robotics, development of automatic drones and self-driving cars. In healthcare, deep learning is used to read CT scans, X-rays and MRIs.
  • Speech Recognition:
    We are increasingly interacting with our computers and smartphones by just talking to them. Machine translation and other forms of language processing are improving each day courtesy of the application of deep learning. Speech recognition has been used in the development of chatbots programs for human-robot interactions for customer service and information acquisition. At WGS, we use IBM Watson to develop AI integrations that can serve your customers using natural language.
  • Customer Relationship Management:
    AI is used to build predictive models which predict the customers who are at high risk of attrition. This will provide you with crucial insights to help you develop a preemptive strategy to minimize churn rates.


In conclusion, many people have equated AI to the industrial revolution due to its momentous and disruptive nature. Using AI, we are developing applications that will help your staff with decision-making recommendations, these decisions can be automated or with approval. This technology has some potential risks, the major one being the likelihood of deskilling people. However, increased automation of menial jobs will enable your staff to shift their focus on the more vital tasks.

For more details on how you can partner with WGS to make use of AI to improve the efficiency of your existing business processes, please contact our team here.

Topics: Mobile Apps, technology, Agile Development, Artificial Intelligence, Business Intelligence

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