Artificial Intelligence:
5 use cases for Finance departments

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Artificial Intelligence: 5 use cases for Finance departments

Get the essential guide to Predictive Finance: everything you need to know on Artificial Intelligence within finance departments.

Companies are increasingly embracing artificial intelligence (AI). It may yet be in its infancy, but AI is set to be a game changer within the digital industry, in the wake of Cloud computing and operational task automation.

Artificial Intelligence: 5 use case for AI in Finance Departments? focuses on AI concrete case studies:

  • How to improve Financial performance using Artificial Intelligence
  • Common uses cases for Artificial Intelligence in Finance (including market performance comparison, boost cash flow, prevent risk, increase turnover through up- and cross-sell & reduce customer loss through churn prediction)
  • Key buying tips when it comes to AI vendors

Artificial intelligence opens new unexpected perspectives to competitive businesses. Following on from the automation and development of internal collaboration via Cloud platforms, artificial intelligence algorithms have advanced to be now far more capable of dealing with the requirements of Big Data.

AI technologies such as Machine Learning or Deep Learning offer autonomous learning based on analysis and data mining. They can now be harnessed to permit companies to better identify undiscovered sources of revenue, to personalize customer relations and to anticipate customer behavior. AI now means that companies can be proactive and to base their decisions on fact rather than intuition. But for this new intelligence to take its place at the heart of a business, the technology must be adapted to existing systems and managed properly.

Key buying decisions with artificial intelligence vendors:

  1. Choose a software vendor who can offer a full suite of predictive services Most customers start with a single use-case such as to boost cash generation. But, to benefit from all the predictive analytics opportunities available, a full suite customer engagement solution is essential so that its application can be rolled out across all company departments.
  2. Build a business case to demonstrate the opportunities to gain Run a feasibility analysis; carry out project planning and preparation; establish your KPIs and goals definition Many AI software publishers will assist you with the implementation of your new solution and advise you on routes to best possible ROI.
  3. Choose a vendor on flexibility, trust and partnership for success Initial project goals can be redefined to better meet the current structure’s needs. This means it is critical that the AI solution you opt for is a flexible and configurable solution (e.g. you may need to add in new data sources). Most importantly, look for a vendor whom you can trust and who has a track record of success. Don’t hesitate to take a look at use-cases and ask for other customer testimonials!


Visit our page on implementing AI, the benefits and use cases within finance teams.

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