Often referred to as the ‘Fourth Industrial Revolution’, the 21st century is paving the way for rapid innovation in robotic process automation and artificial intelligence technologies – collectively known as intelligent automation.
To put this in perspective, experts predict the intelligent automation market will scale to a $30 billion valuation by 2024, partly due to its spectrum of applications. The banking industry, in particular, benefits from a range of use cases for intelligent automation. In fact, according to research from Futurum, 85% of banks have used intelligent automation to automate core processes.
However, only automating back-office processes ignores the true extent of AI's capabilities. Recent AI insights suggest that no task is off-limits for automation. In other words, it’s no longer repetitive manual tasks that are primed for AI technology – there is now a virtually limitless range of applications for intelligent automation.
In this article, we’ll cover several examples of intelligent automation in banking and the benefits that intelligent automation brings to the table.
Detecting fraudulent activity in real time is a prime example of intelligent automation in the banking sector. After training with ample high-quality data, AI algorithms can detect anomalies, such as financial misconduct. AI is deployed end-to-end in automated fraud detection systems – from identifying potential fraud, alerting the customer and assessing the validity of their response.
Furthermore, artificial intelligence’s marriage of computer vision, Optical Character Recognition (OCR) and machine learning algorithms also make AI software an unrivalled tool for detecting tampering on financial documents. Document fraud can take many forms invisible to the naked eye – another area where intelligent technology is an invaluable asset.
During Evolution AI’s webinar this year, our CEO, Dr. Martin Goodson, summarised the capacity of intelligent automation, particularly using automated data extraction for credit scoring:
Traditional credit risk scoring for financial statements – income statements and balance sheets – is very time-consuming, and you need a highly-trained workforce to extract information and create risk models and ratios.
Now, with generative AI technology, this can all be automated. It's relatively straightforward now to extract from an income statement or balance sheet and calculate ratios, but most importantly, I'm not just talking about data extraction: I'm referring to much more sophisticated financial analysis. It can actually do the analysis rather than just the extraction.
In other words, using AI for lending can fulfil several essential functions, allowing companies to assess creditworthiness instantly and approve or deny loan requests (or flag for manual review).
Of course, intelligent automation for instant loan approvals benefits anxious customers but also bored staff. Along the way, AI can collect information about the loan decisioning process, unlocking insights about how to make this process more efficient.
Banks receive volumes of customer support requests, inundating their staff with rote busy work. Many of these inquiries are automatable, such as account balance checks or transaction history reviews.
Besides responding to simple requests from customers, AI can also produce analytics such as sentiment analysis. Collecting data can also streamline the delivery of personalised banking solutions. For instance, customers who have bought plane tickets will be far more receptive to travel insurance quotes and currency exchange offers.
Reconciliation is a time-consuming process with high stakes, making it ripe for intelligent automation. AI’s ability to process huge volumes of data and quickly identify patterns and anomalies makes it an ideal tool for oversight. As AI can quickly learn from its mistakes, its accuracy will only improve over time.
You’ll experience immediate and long-term cost savings from intelligent automation. Let’s examine how intelligent automation can deliver:
Some assume that the complexity of AI technology can only mean complex integration (and frequent calls to the IT department). However, many AI technologies – packaged as SaaS – boast smooth methods of implementation, such as integration via API or even directly uploading documents to the software’s interface.
Usability is also a concern. For one, introducing AI into the workplace can cause friction from employees unwilling to commit to new and unfamiliar processes. Consequently, it’s important that the technology in question is as user-friendly as possible.
Jessica O’Hare, Head of Product at YouLend, described implementing intelligent technology to automate credit decisioning as removing "the parts of the job that people don’t like,” adding that “no one wants to sit around extracting information from a bank statement.” Employees will perform better when given a stimulating workload that utilises their qualifications.
A report by Clockify shows that up to 90% of workers spend time on repetitive, manual tasks that are fundamentally unenjoyable. Eliminating these tasks creates a more driven and focused workforce.
Our company has worked alongside banks, such as NatWest, the Royal Bank of Scotland and DF Capital, to implement intelligent automation in the form of automated data extraction from financial documents.
Data extraction serves a vital function for the vast majority of companies in the financial services industry. Companies are rapidly adopting AI software for data extraction as a cost-effective and faster alternative to OCR and manual data capture.
A question that we sometimes receive is whether our technology could become obsolete, in the same way that AI is rendering legacy technologies OCR obsolete. The answer is yes – without committed and continuous innovation.
By 2030, research projects the AI market could reach a two-trillion-dollar valuation. Across the world, companies are pouring billions of dollars into advancing artificial intelligence while packaging it into enterprise-ready solutions. Consequently, back-office solutions like automated data extraction will continue to become even more intuitive and commercially available.
In recent years, intelligent automation in banking has evolved from a novelty to a necessity. Across all industries, companies are deploying AI as a tactical advantage, but it is in banking that automated AI functionality can really shine.
Overall, intelligent automation may take careful planning and implementation. At its best, however, it is scalable, cost-effective and reliable.
If you’d like to learn more about how automated data extraction can optimise your business’s revenue streams, see our case studies or speak to one of our experts in a demo.