Home Finance Artificial Intelligence Provides Back Office Support for Regulatory Reporting Requirements

Artificial Intelligence Provides Back Office Support for Regulatory Reporting Requirements

Financial reporting is a headache most companies could do without. However, for many it’s not a case of wanting to do it, but having to. Since 2009, the UK’s Financial Conduct Authority (FCA) has levied more than £30 million in penalties relating to inaccurate or inconsistent financial and trade reporting. The problem is that there’s no set procedure in place. While some firms are happy to complete the reporting in-house, others hire third parties that know very little about the business. So, if this is the case, why not use AI to help?

Artificial intelligence (AI) is being used in almost every industry you can think of. It’s already being used within the financial services sector, so why not use it in regulatory reporting too? Firstly, AI could be used to help eliminate the backlog of checks that need to be carried out. Developing advanced algorithms will help with data mapping and producing auto generated documentation. Using AI also means that business users can oversee these processes, opposed to technical experts.

Another use of AI within this field is to analyse and match patterns of potential candidates based on their previous activities. This machine learning technique has been proven to increase automated matches by as much as 20%. It’s been demonstrated time and time again just how efficient AI is most circumstances and it’s the firms that don’t learn to adapt to it that will suffer.

Automated Data Integrity Services would significantly help when it comes to the improvement of financial reporting practices. It will ensure more accuracy and will help firms to deliver better audits and control while improving efficiency levels at the same time by taking away that manual element. However, there are four hurdles that must be overcome first before the adoption of widespread data integrity services can take place.

They are:

  • It takes time and money to create new integrity checks which can add to the manual backlog.
  • Being able to monitor the delivery of a large amount of data integrity is both expensive and time consuming.
  • Environmental changes are responsible for the natural degradation of automation over time.

Any exceptions that are found in doing the integrity check must be managed, monitored, and resolved manually.

Once these challenges have been overcome, there’s no doubt that AI will significantly help this industry. But, until then, it’s business as normal.

Source InternationalBanker

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KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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