Building an AI-Powered Price Prediction Engine to Benefit Businesses Across Industries

machine learning technologies and algorithms


historical rates of different macroeconomic indicators considered


influencing factors considering in predictions


years to build a Universal Machine Learning Framework

Table of contents

The Background

An up-and-coming FinTech company set its sights on an AI-powered prediction system that could help businesses optimize their pricing strategies. The solution was meant to cater to raw materials suppliers, for starters, in the agricultural sector. Later, the client planned to extend the technology to other industries.

A price prediction tool was expected to help farmers and commodity traders minimize the risk of pricing fluctuations. It would indirectly mitigate the adverse effects of the fluctuating agricultural commodities’ prices on a country’s GDP.

The solution was supposed to leverage machine learning to make quick and accurate predictions. The system had to be capable of working through massive data sets and millions of influencing factors.

The ultimate goal of that project was to provide enterprises across industries with a prediction tool to make strategic pricing decisions even in the most volatile markets. This, in turn, was slated to boost sales efficiency and revenue growth.

The Challenge

Prediction tools commonly use an analytics framework that covers only customer profiles and historical transactions. The FinTech startup intended to work on a much larger data array.

The main challenge was to design a system that could effectively classify and analyze large data sets coming from many different sources. The startup needed to structure data to prevent prediction errors and biases.

Moreover, the client wanted to get a sophisticated tool providing both predictions and recommendations.

The development team confronted these challenges by grouping tasks into three main types:

  • Data mining or poring through large data sets to identify patterns and relationships

  • The ETL (extract, transform, load) process to integrate data from multiple sources

  • Big data analytics and data science to extract meaningful information from the data sets

The team started out by working on data sets that involved thousands of macroeconomic indicators.

While developing the predictive models, the engineers realized that their well-trained algorithms were producing faulty results. After careful examination, they solved the problem by building several machine learning algorithms to check and verify each other’s results.

Clearly, the right development team proved instrumental to the project’s success.

Strategic Partnership

While gearing up an in-house development team for the project, the FinTech startup decided to hire a Data Science expert to lead the team. That’s when Pwrteams entered the scene.

We selected just the right person out of our rich talent pool — a data science expert with MBA and Ph.D. credentials strengthened by decades of industry experience. He is as adept at business matters as he is at technical problem-solving.

Our Data Science expert proved his mettle and was eventually promoted to top management position — Head of Data Science Office. He is responsible for the following:

  • The system’s architecture

  • The software’s AI features

  • The tool’s business value

  • Consultations and negotiations with partners and clients

Our Data Science expert was instrumental in putting together a development team complete with talented developers, experienced AI technologists, and competent researchers. A total of 13 people have worked to connect worldwide data sources, providing effective and transparent commodity price predictions to the clients.

Also Pwrteams Data Science expert took part in the pre-sales activities and negotiations with potential partners and clients.

The Results

Under the leadership of our Data Science expert, the development team built an elaborate AI-based system made up of several machine learning engines. The tool uses a problem classification algorithm and supervised learning framework to predict pricing, forecast market trends, and provide real-time analysis of market volatility.

The system offers numerous practical use cases, which makes it ideal for businesses needing an all-round data analysis solution. It is especially beneficial for companies operating in dynamic markets. Here are some of the use cases:

  • Financial services:
    Predict financial market trends and create credit risk models.
  • Retail:
    Market products to the right audience at the right price point.
  • Manufacturing:
    Forecast demand to deliver the right amount of raw materials at the right time.
  • Energy:
    Predict the impact of equipment failure, weather conditions, and new laws on service costs.
The FinTech company succeeded in creating the ultimate data analysis solution that stands to benefit businesses across industries.

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