3 Main Use Cases of Artificial Intelligence in Finance and Accounting

Can accountants leverage AI to streamline their processes?

Like in other industries, artificial intelligence in finance and accounting is gaining a lot of interest.

In fact, according to a New Economist Intelligence Unit report, 77% of respondents believe that unlocking the value of artificial intelligence will be the difference between winners and losers among banking institutions.

Owing to the sensitivity of their jobs, which include monitoring transactions and forecasting investments, finance leaders and accountants have to carry out their tasks with a lot of caution .

Implementing AI technologies that leverage natural language processing and machine learning is therefore critical to streamlining and optimizing finance and accounting related tasks.

This is  due to their ability to examine numerous data points and find patterns that may go unnoticed by the naked human eye.

In this article, we’ll discuss 3 main use cases of artificial intelligence in finance and accounting.

Let’s get started.

1. Operations Automation

Financial institutions, such as banks have a plethora of tasks to carry out to meet their targets, like investment goals and credit management.

However, most of these operations, such as traditional bookkeeping and accounting involve manual data entry that take a lot of time and effort. 

In addition to that, it is repetitive and prone to errors that put the financial institutions at risk of losses that ultimately affect their ability to serve customers effectively.

It is, therefore, essential that companies automate their operations in finance and accounting departments to eliminate the redundancy caused by the repetitive nature of data entry.

Leveraging artificial intelligence enables computers to take on repetitive tasks, such as entering data, inputting and matching data from invoices and receipts to transactions, reconciling accounts, and tracking debts. 

By introducing AI-powered invoice capture technologies that apply machine learning, financial institutions can automate their billing systems and remind customers to finance their loans.

Actually, according to a PYMNTS AI in Focus study, 79% of large banks are using AI to manage credit risk. This way, they can reduce financial costs, reduce loan recovery delays, and eliminate errors due to manual credit risk management.

In addition to that, companies that use artificial intelligence to extract financial data from bank statements and cross-check it with complex spreadsheets through intelligent document processing can significantly accelerate account reconciliation processes and eliminate errors that cause major interruptions. 

The corporate side of AIB was able to improve the information management, reduce reports and budgets production time, and increase the overall data accuracy by sourcing an intelligent solution, Codec, for their operations.

2. Regulatory Compliance

Financial institutions operate under strict regulations owing to the sensitivity of the data they collect, including customer names, dates of birth, and financial information.

However, the large volumes of unstructured data in the form of text files from sources like customer transactions and expenditure reports have contributed to some of the most significant challenges such as fraud due to data breaches.

Actually, according to a survey by KPMG, over 50%  of the respondents globally experienced an increase in fraud volume due to identity theft, cyber attacks, and push payments scams.

Preventing fraud and complying with strict regulatory requirements are therefore among the main use cases of artificial intelligence in finance and accounting that are critical to protecting customers and the institutions themselves from potential losses.

Fortunately, artificial intelligence powered technologies have advanced significantly to aid in maintaining data systems secure and detecting possible fraudulent actions.

Through machine learning algorithms, AI technologies can monitor all the data coming into financial institutions, analyze and discover hidden insights, patterns, and trends to get invaluable information on possible attacks or data breaches.

By leveraging natural language processing, AI technologies also have the ability to browse through many documents rapidly, extract relevant data, and scan for non-compliance instances without manual intervention.

They cross-check this information in accordance with accounting regulations and flag non-compliant instances for financial leaders to double-check.

A case study by DZ Bank reveals that the bank was experiencing challenges in detecting advanced cyber threats and distinguishing benign anomalous attacker behavior.

Upon leveraging an easy-to-use AI powered analysis platform, DZ bank was able to detect threats automatically and trigger alerts to speed-up responses while complying with strict data privacy regulations .

3. Customer Service

Customer expectations are soaring across industries, including finance and accounting.

Because of technological advancements that have enabled customers to receive instantaneous and personalized services, customers will often choose the business that can give them the same experience.

Unfortunately, many financial institutions are facing challenges in meeting customer demands due to the use of outdated voice-centric contact systems in an age of social media and web chats.

This makes it difficult for finance leaders to engage with customers, track their needs, and predict when they will most likely strike an interaction. As a result, customer loyalty and retention is waning as churning continues to increase.

According to Gallup research, retail banking customers who are fully engaged bring 37% more revenue than those who are disengaged.

By leveraging artificial intelligence technologies like machine learning and natural language processing, financial institutions can find anomalous patterns through data extraction and processing. 

They can also identify areas that are at risk in their Know Your Customer (KYC) processes without human intervention.

Implementing conversational AI systems, such as Chatbots, into customer service tasks ensures that customer requests are addressed faster, ultimately boosting personalization and reducing the workload in call centers.

In addition to that, AI technologies can analyze patterns in customer behavior and predict potential churning. This allows financial institutions to take action swiftly to prevent it.

Bank of the West was planning to enter a new market and needed to meet the highest level of customer experience to put them over the edge in the ever-changing market.

Upon partnering with SuiteCX, the team was able to compare banks and map the customer journey by determining their unique pain points for an enhanced experience.

Conclusion

Artificial intelligence is clearly revolutionizing the finance and accounting industry.

By leveraging technologies, such as natural language processing and machine learning, it automates operations, boosts customer service, and enhances regulatory compliance.

In addition to that, it allows you to acquire data driven-insights to streamline financial and accounting processes.

According to a survey by EY Global FAAS, over 70% of leaders in accounting, financial, and advisory services believe that AI will generate data-driven insights for companies.

Having gone through the main use cases of artificial intelligence in finance and accounting, I hope you can now implement it in your business to analyze data and discover hidden insights.

If you’re just getting started, I would recommend incorporating a Chatbot for better customer engagement.