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The world of finance is rapidly changing. As finance institutions continue to implement quant strategies, the need for data science and machine learning skills continues to grow. The Financial Data Professional Institute (FDPI) is answering this need with a designation in data science for finance professionals in an effort to boost and integrate quant knowledge into analysts’ skills.

No matter what your role is, earning your FDP Charter can make a difference. Our curriculum offers the competitive advantage to many roles within a financial institution:

  • Analysts: Use new data sources and machine learning tools to evaluate and predict.
  • Risk Managers: Use machine learning algorithms to develop more robust risk measures more rapidly.
  • Asset Managers: Perform due diligence on funds that employ machine learning/artificial intelligence algorithms.
  • Accountants: Applications of machine learning/artificial intelligence to perform textual analysis of reports.
  • Underwriters: Apply machine learning using big data to improve risk analysis in the insurance industry.
  • Operations Managers: Use machine learning/artificial intelligence tools to reduce cost (operational alpha) while offering better customer service.
  • Human Resources: Want to train and retain top talent? Offering advanced data science classes to current employees is a sure way to retain good employees.


The Role of Business Translators

With this disruption in traditional banking models, interest in data science is growing. The data scientist creates the statistical models, trains the models and then applies the models to real-life situations. By tracking patterns of customer activities and looking for unusual data points such as people accessing accounts at strange times, for example, machine learning can predict which transactions may be fraudulent. But hiring pure technical analytics talent is only the first step.

According to the McKinsey Global Institute, financial institutions must also find and train business “translators,” who help data scientists understand business problems and ensure that analytics insights are communicated back to business units. These translators must be fluent both in the business language and the data scientist language.

"Financial institutions must also find and
train business “translators,” who help data scientists
understand business problems and ensure
that analytics insights are communicated
back to business units.

These translators must be fluent both in the business language and the data scientist language."

McKinsey Global Institute


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