In the current climate, it is difficult to find an industry that hasn't been impacted by AI (Artificial Intelligence). However, the impact of this technology is undeniable within financial institutions.
When utilised correctly, AI tools can drive innovation and automation across the sector, making it easier to perform tasks, lower costs and address privacy concerns (among other benefits.) For example:
- The material benefits of the technology are becoming increasingly apparent, with the aggregate potential cost savings for banks alone estimated at a colossal $447 billion.
- AI can help lenders make more accurate decisions concerning loan approvals and risk assessments, cutting losses by an estimated 23% annually.
- AI applications can reduce the workload for finance teams by as much as 20%, freeing resources to focus on more complex and strategic endeavours. Considering the complex issues tied to workplace burnout, such as low productivity rates and high employee turnover, this can be a game changer for financial institutions.
In short, AI is rapidly becoming a core component of the modern financial landscape. As such, it's hardly surprising that a recent McKinsey report found that, while AI adoption remains steady at around 55%, more than 75% of respondents claim that their companies plan on increasing their investments in AI moving forward.
In this article, we'll take a closer look at how AI and new technologies are transforming the AI industry, the impact this will have moving forward, and how your business can leverage AI to stay competitive.
AI applications can reduce the workload for finance teams by as much as 20%, freeing resources to focus on more complex and strategic endeavours.
The emergence of AI in finance
While it may seem to be a relatively new concept, AI has been around for decades. However, recent advancements in natural language processing (NLP), machine learning, and big data mean that these systems have made their presence known within the finance world.
While it had been a topic of discussion among developers since the 1950s, the first adoption of AI in finance occurred as far back as the 1980s. During this time, Edward Feigenbaum's "Expert System", a computer system designed to "emulate the decision-making ability of a human expert," was used to prevent fraud by identifying fraudulent transactions and credit card charges.
In the early 2000s, AI was used in a new fashion within the financial industry due to the emergence of smart chatbots and digital assistants. While this was initially met with some backlash, a recent study found that "69% of consumers are comfortable seeking advice from virtual assistants," even when it comes to finance.
With the scope and range of data now available to financial organisations each year, AI is being utilised by financial firms for various tasks, such as:
- Risk management.
- Market analytics.
- Fraud detection.
- Customer support.
More recently, banks and financial organisations have been subject to a growing demand for more personalised customer service. In fact, this is something that 70% of customers consider to be "important." AI can meet their personalisation demands without compromising the security of customer data, something that is crucial within the banking sector.
The widespread benefits of AI in financial services have led to 85% of finance businesses developing an AI adoption or AI expansion strategy.
Find out more about where AI in finance is headed during this panel discussion hosted by AI industry experts from the ICAEW, Chaser Zeitios, and Aquilla as they discuss what the future of finance will look like with the rise of artificial intelligence.
AI's multifaceted role in finance
One of the greatest strengths of modern artificial intelligence applications is their ability to address multiple challenges simultaneously. As a result, it can be used to great effect within both retail and corporate banking applications.
Improved customer experience
For example, using AI, or smart chatbots, can be used to automate time consuming tasks relating to customer service. In an industry where employees are required to work as quickly as possible, this kind of artificial intelligence-enabled software can make a real difference. In fact, one study found that a third of those using digital assistants said they were "saving between 30 minutes to an hour of time each workday."
Simply put, replacing low-skilled manual tasks, such as data entry and document management, with automated bots also helps improve business efficiency and cuts down on operational costs.
However, this also serves to benefit the customers by boosting customer satisfaction. They receive prompt responses to their queries, as opposed to having to wait around for hours for a response - even if they are sending a message in the middle of the night.
This improves the customer experience while freeing up resources and training staff to focus on more complex tasks.
Reduced risk
Using machine learning algorithms can streamline loan processing and reduce risk by up to 40%. Big data analysis can help identify patterns for more accurate market predictions or fraud detection, which can help prevent money laundering and other forms of financial crime. This is because the tools make creating accurate reports and analysing past data easier.
This can help those operating within the financial services industry to better their risk management strategies and protocols for the betterment of their customers and the business.
Making accounts receivables easier with AI
Financial institutions can also improve their accounts receivable process through the US of AI technologies.
With AI-driven automation, businesses can streamline the entire process, from invoice creation to payments. This is particularly beneficial for those who must process large volumes of invoices at once. AI can also recognise patterns in customer payment or purchase behaviour. Not only can this data be used to identify the "early signs" of non-payment or delayed payments, but this data can be used to optimise collection strategies moving forward.
Chaser's market-leading AI-driven credit control platform is helping thousands of businesses save time and money by automating the process of chasing outstanding customer payments. By utilising AI, Chaser has introduced a range of new features that are changing the way businesses, including financial institutions, approach their finances, including:
Recommended chasing times
Knowing when the most effective time to chase customers for payment can prove to be challenging, which is perhaps why 87% of businesses are paid late.
Chaser's AI-driven software offers recommended chasing times. Based on the customer's payment history, such as when they typically open emails or process payments, this tool lets businesses send payment reminders at the optimal time (i.e., when customers are most likely to pay), maximising their collection efforts.
By employing these AI-driven and data-backed insights, Chaser customers can rely on Chaser's track record of optimising communication strategies. This means they can identify when customers are most likely to respond and take action, meaning they can collect payments faster. This reduces the amount of time that their accounting team will spend chasing delayed payments, meaning that it could also be a great way to boost productivity and cut operational costs.
Late payment prediction
Taking a proactive approach to collections can also be difficult, especially when dealing with a large customer base - something which is common in both the banking and fintech industries.
However, Chaser's AI-driven Late payment predictor tool lets businesses know which invoices will likely be paid late instantly by analysing a range of data points in seconds. Thanks to these accurate predictions, finance teams can begin to focus their efforts on the invoices that are most likely to be paid late.
By taking early action and engaging in proactive collection strategies, organisations can drastically reduce the amount of time spent chasing overdue payments and effectively minimise the risk of any late payments in the future. This ensures that they remain on track to meet their financial goals.
Beyond accounts receivable: AI's expansive impact
Beyond accounts receivable, AI is having a profound impact on the broader financial sector.
After all, this method of digital transformation is revolutionising how businesses within the financial industry operate in an ever-changing global economy, from automated trading and algorithmic pricing to AI-driven market predictions.
For example, technological advancements mean that machine learning algorithms can detect patterns in unstructured data that would be difficult for humans to interpret. This means that AI models can be used to help businesses identify financial trends before they become apparent to the general public.
At the same time, AI models and generative AI allow businesses to take personalisation to the next level, which is again achieved through analysing customer data. For example, this allows banks to tailor their offerings to individual customers' needs more accurately and efficiently than ever before.
Unleashing strategic potential
As outlined above, one of the most essential benefits of using AI applications within financial institutions is that it frees up resources and staff, who are then able to focus on more complex tasks.
In finance, this means freeing up analysts and traders from mundane tasks, such as data entry and document management, allowing them to instead focus their efforts on developing new strategies for the company's future.
Additionally, by leveraging AI-driven insights, businesses can make more informed decisions and identify new growth and development opportunities. For example, AI tech can analyse a range of data to identify trends in customer behaviour, without breaching any financial regulations so that companies can adjust their product or service offerings accordingly.
With access to AI-driven market analytics, businesses can also identify new opportunities to capitalise on emerging trends and untapped markets. This allows those in the financial industry to maximise their ROI (Return on Investment), create a more profitable future, and reach their financial goals.
Addressing challenges and ethical considerations
As with an emergent technology, particular challenges and ethical considerations must be addressed.
AI is no different, and with 52% of adults admitting to being "distrustful" of AI, organisations must consider the technology's legal implications. For example, while AI-driven systems can quickly process large amounts of data, care must be taken to ensure that customer privacy is not compromised as a result of this.
Organisations must also understand potential biases in their algorithms or datasets that might lead to inaccurate decision-making. For example, data biases could play a role when it comes to making credit decisions, especially regarding credit scoring.
Responsible AI adoption requires transparency and accountability across the board so that it does not become a dangerous or disruptive technology. As such, organisations should take the necessary steps to ensure their AI systems comply with the relevant regulatory requirements and ethical principles. This is an effective risk management strategy that reduces systemic risks.
The IEEE Global Initiative is an example of an organisation taking the lead in responsible AI governance. The initiative's focus on practical, actionable guidelines and standards provides organisations with a comprehensive framework for developing and managing responsible AI systems.
Future horizons: AI in finance
Artificial intelligence is a still-developing technology which has already shown promise within the finance industry. Moving forward, the influence of AI in financial services will only become more profound.
As such, organisations that act quickly to embrace AI-driven solutions will be well-placed to take advantage of its benefits and drive future growth. AI-enabled automation, predictive analytics, fraud prevention, personalisation, and improved decision making are just a few areas where AI can have a transformative impact on how businesses approach their finances.
The possibilities are endless, and organisations can gain a competitive edge by embracing the vast amount of AI tools, including Chaser's expert software.
However, they must also be aware of the potential risks and take the appropriate steps to ensure their AI systems comply with relevant regulations and ethical principles.
By taking advantage of AI's opportunities while following best practices, organisations can unlock their strategic potential, develop a better understanding of financial data, and create a more profitable future for their business.
Unlock your businesses' potential with AI and Chaser
Chaser's software is a prime example of the innovative solutions that can be created when AI is employed in finance. By automating and streamlining accounts receivable processes, Chaser has helped businesses save time, money, and enhance customer experiences. In short, it's an excellent way to upgrade a business's financial system.
AI is here to stay, and finance is no exception. With its immense potential to transform the industry, AI-driven finance solutions are only set to become more prevalent in the coming years, and keeping up to date with the technology can give businesses offering financial services a vital competitive edge.
To find out about where AI in finance is headed, join this panel discussion hosted by AI industry experts from the ICAEW, Chaser Zeitios, and Aquilla as they discuss what the future of finance will look like with the rise of artificial intelligence.
You can also learn more about how Chaser's AI-driven technology can help your business maximise its return on investment and reduce the risk of late payments by arranging a demo with one of our experts or signing up for your no-obligation 10-day free trial. Join the vast amounts of businesses transforming their business with AI today!
