{"id":804,"date":"2018-07-18T02:57:58","date_gmt":"2018-07-18T02:57:58","guid":{"rendered":"http:\/\/kusuaks7\/?p=409"},"modified":"2023-07-25T17:13:20","modified_gmt":"2023-07-25T17:13:20","slug":"machine-learning-in-finance-why-what-and-how","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/software-ux-ui\/machine-learning-in-finance-why-what-and-how\/","title":{"rendered":"Machine learning in finance: Why, What & How"},"content":{"rendered":"

Ready to learn Machine Learning? Browse courses<\/a>\u00a0like\u00a0Machine Learning Foundations: Supervised Learning<\/a> developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

Machine learning in finance may work magic, even though there is no magic behind it (well, maybe just a little bit). Still, the success of machine learning project depends more on building efficient infrastructure, collecting suitable datasets, and applying the right algorithms.<\/p>\n

Machine learning is making significant inroads in the financial services industry. Let\u2019s see why financial companies should care, what solutions they can implement with\u00a0AI and machine learning, and how exactly they can apply\u00a0this technology.<\/p>\n

Definitions<\/h2>\n

We can define\u00a0machine learning (ML) as a subset of data science<\/strong>\u00a0that uses statistical models to draw insights and make predictions. The chart below explains how AI, data science, and machine learning\u00a0are related. For the sake of simplicity, we focus on machine learning in this post.<\/p>\n

The magic about machine learning solutions is that they learn from experience\u00a0without being explicitly programmed<\/strong>. To put it simply, you need to select the models and feed them with data. The model then\u00a0automatically adjusts its parameters to improve outcomes.<\/p>\n

Data scientists train machine learning models with existing datasets and then apply well-trained models to real-life situations.<\/p>\n

\"The<\/p>\n

The model runs as a background process and provides results automatically based on how it was trained. Data scientists can retrain models as frequently as required to keep them up-to-date and effective.<\/p>\n

In general, the\u00a0more data<\/strong>\u00a0you feed, the\u00a0more accurate<\/strong>\u00a0are the\u00a0results<\/strong>. Coincidentally, enormous datasets are very common in the financial services industry. There are petabytes of data on transactions, customers, bills, money transfers, and so on.\u00a0That is a perfect fit for machine learning<\/strong>.<\/p>\n

As the technology evolves and the best algorithms are open-sourced, it\u2019s hard to imagine the future of the financial services without machine learning.<\/p>\n

That said,\u00a0most\u00a0financial services companies<\/a>\u00a0are<\/strong>\u00a0still\u00a0not ready<\/strong>\u00a0to extract the real value from this technology\u00a0for the following reasons:<\/p>\n

    \n
  1. Businesses often have completely unrealistic expectations towards machine learning and its value for their organizations.<\/li>\n
  2. AI and\u00a0machine learning research and development<\/a>\u00a0is costly.<\/li>\n
  3. The shortage of DS\/ML engineers is another major concern. The figure below illustrates an explosive growth of demand for AI and machine learning skills.<\/li>\n
  4. Financial incumbents are not agile enough when it comes to updating\u00a0data infrastructure.<\/li>\n<\/ol>\n

    \"Talent<\/p>\n

    We will talk about overcoming these issues later in this post. First, let\u2019s see why financial services companies cannot afford to ignore machine learning.<\/p>\n

    Why consider machine learning in finance?<\/h2>\n

    Despite the challenges, many financial companies already take advantage of this technology. The\u00a0figure below shows that financial services\u2019 execs take machine learning very seriously, and they do it for a bunch of good reasons:<\/p>\n

      \n
    1. Reduced operational costs thanks to process automation.<\/li>\n
    2. Increased revenues thanks to better productivity and enhanced user experiences.<\/li>\n
    3. Better compliance and reinforced security.<\/li>\n<\/ol>\n

      \"Finance<\/p>\n

      There is a wide range of open-source machine learning algorithms and tools that fit greatly with financial data. Additionally, established financial services companies have substantial funds that they can afford to spend on state-of-the-art computing hardware.<\/p>\n

      Tanks to\u00a0the quantitative nature of the financial domain and\u00a0large volumes of\u00a0historical data, machine learning is poised to enhance many aspects of the financial ecosystem.<\/p>\n

      That is why so many financial companies are investing heavily in machine learning R&D. As for the laggards, it can prove to be costly to neglect\u00a0AI\u00a0and ML.<\/p>\n

      What are machine learning use cases in finance?<\/h2>\n

      Let\u2019s take a look at some promising machine learning applications in finance.<\/p>\n

      \"Machine<\/p>\n

      Process Automation<\/h3>\n

      Process automation is one of the most common applications of machine learning in finance<\/strong>. The technology allows to replace manual work, automate repetitive tasks, and increase productivity.<\/p>\n

      As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale up services. Here are automation use cases of machine learning in finance:<\/p>\n