The new era of intelligent automation in financial services

Prepare your financial services institution to take advantage of intelligent automation, from robotic process automation to generative AI.

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The new era of intelligent automation in financial services


Prepare your financial services institution to take advantage of intelligent automation, from robotic process automation to generative AI.

The new era of intelligent automation in financial services

In recent years, we have experienced a plethora of developments in intelligent automation, and each innovation represents a massive opportunity for financial services institutions. In fact, The Economist found four in five banking executives agree that unlocking value from artificial intelligence (AI) will ultimately “distinguish the winners from the losers.” As explained by Michael Berns, AI & FinTech Director at PwC Germany:

“AI is going to be a key competitive factor for financial institutions in the future, but it also offers other applications far beyond process automation.”

When it comes to ushering in the new era of intelligent automation in financial services, banking executives see many potential use cases. According to a PwC survey:  

  • 79 percent of executives want to increase digital efficiency in their business processes.
  • 73 percent want to make general cost savings.  
  • 50 percent expect AI to help their company better comply with regulations.

To improve your data-driven decision making and keep one step ahead of the competition, however, it requires a blend of the right intelligent technology solutions, and the right support to help you deploy them. This is where intelligent automation comes in.

In this blog, we explore the new era of intelligent automation in financial services, helping you to better understand the technologies that are driving banking innovation.

What is intelligent automation in the context of FSIs?

Intelligent automation is the answer to customer demand for efficient, high-quality financial services that solve real-world problems. With advanced hacking techniques, for instance, it’s becoming a must-have for things like account fraud prevention and staying on top of data security. This is especially important given the average cost of a data breach in the financial industry was nearly six million USD in 2022.

Moreover, intelligent automation helps you build pattern-recognition-based forecasting into your operations so you can make proactive decisions that future-proof your firm. In short, it is what happens when automated processes meet artificial intelligence.

In today’s terms, intelligent automation is defined by rapidly advancing technologies such as robotic process automation, machine learning, and generative artificial intelligence.

Let’s now look at some of these use cases of intelligent automation.

1. Robotic process automation (RPA)

“Put simply, the role of RPA is to automate repetitive tasks that humans previously handled. The software is programmed to do repetitive tasks across applications and systems. The software is taught a workflow with multiple steps and applications.”
Antony Edwards, COO at Eggplant

RPA is a key part of streamlining modern business process management across a range of services. This is a great place to start when considering intelligent automation because you can roll out specific automated processes that make a tangible difference to your customers and employees at points of friction.

At Modes, we rely on Mulesoft as our tool of choice for RPA. Not only does it help you configure RPA processes, but the RPA Manager helps you identify those friction points where RPA would be most beneficial, so it’s automated planning and implementation in one platform.

Saying that, we know it’s helpful to have an idea ahead of time about how such technologies might be deployed.

Here are some use cases:

KYC (Know Your Customer)

Know Your Customer is not only a regulatory imperative, but it's good business, too. Executing this vital function is, however, time-consuming and repetitive. It’s rote work that doesn’t engage ambitious employees.

With RPA, you can automate the verification of customer identity and flag potential issues.

Regulatory compliance

Speaking of must-haves, RPA can be used for several processes related to regulatory compliance. RPA is best suited to maintaining accuracy and consistency, while your team is best put to work building relationships with customers. Instead of risking compliance through human error, RPA can ensure nothing is lost, miscategorized, or poorly organized.

For example, RPA can archive records or data appropriately, preserving the audit trail to ensure compliance over the long term, and then automate the time-consuming reporting and documentation required during an audit. These kinds of processes protect your reputation as a business and help you avoid expensive fines.

Account opening, maintenance, and standard query resolution

From onboarding new account holders, to operations like updating contact information, RPA handles certain key account procedures and queries.

Anything that follows a step-by-step set of actions can be taken over by RPA, leaving human agents free to take care of more complex inquiries. That makes it perfect for standard processes like these. Implementing RPA here impacts some of the most critical metrics for customer experience and employee efficiency, such as time-to-open for accounts.

2. Machine learning (ML)

“Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations[…] The impact of machine learning will be of this type — quietly but meaningfully improving core operations.”
Jeff Bezos, CEO of Amazon

Machine learning operates beneath the surface, creating intuitive, prediction-based experiences for employees and customers.

Here are some use cases:

Fraud detection

ML can parse patterns and behaviors to prevent fraud and money laundering. Where you would not be able to process such patterns yourself, machine learning can spot signs of suspicious activity, report them, and even act on them (for example, by freezing transactions) before they become more widespread.

Given imposter scams are the most prevalent fraud type in the US, with 178,040 incidents reported during the first quarter of 2023, this is an important issue that needs addressing.

Credit risk modeling

ML can use huge data sets to analyze and predict the likelihood that a customer will default on a loan or mortgage, going beyond standard credit checks. This means you can take a more proactive approach to reducing the risks of negative amortization and mortgage holders falling into arrears.

Portfolio management

For investment managers looking to strategically allocate assets, the ability to more accurately predict market trends — without having to manually sift through company filings, macroeconomic reports, or other sources — is a must-have.

While there are some risks, this study out of The Journal of Financial Data Science concludes ML techniques show great promise for active portfolio management.

Predicting churn

By identifying at-risk customers early (based on things like transaction history or demographics), FSIs can take a proactive approach to reducing churn, such as by offering incentives.

As ML sees specific triggers that lead to higher churn rates — like dissatisfaction with a fee change — this allows you to measure the value of that particular change versus the impact.

3. Generative Artificial Intelligence (AI)

Generative AI creates tailored, personalized experiences. Daniel Ordibehesht, Senior VP and Head of Strategy at ATB put it best in an interview with Modes:

"The binary of humans as 'personal' and software as 'impersonal' is false. It’s actually an execution challenge."
Daniel Ordibehesht, Senior VP and Head of Strategy at ATB

If someone calls a bank and is kept on hold for hours before speaking to a representative for a simple query, that feels quite impersonal. Whereas, if they can get their question answered in minutes by a friendly AI that solves the problem and “feels human”, that can seem like a more personal experience. (All while minimizing the wait time on the phone for other inquiries.)

Executed well, generative AI empowers the human-touch approach rather than takes away from it. Here are some use cases:


Specifically, sophisticated Natural Language Processing bots can answer questions and offer personalized financial advice. For example, you can provide credit card offers or investment recommendations based on a customer’s risk tolerance and goals.

Of course, you need to deploy chatbots within certain parameters. It’s vital to ensure AI-generated responses are valid and trustworthy. Customer data must be kept within the boundaries of the interaction, and you need guardrails to prevent AI hallucinations. Technologies like Salesforce’s Einstein Trust Layer can help here.

Interaction and document summaries

Sometimes, you just need a summary. Customers may prefer to see a clear summary of long financial documents before diving into the details so they have a more grounded starting position.

Advisors can use generative AI to summarize the key points within a conversation, email thread, or long documents. For example, parsing a complaints history in minutes so agents can solve the problem without spending too much time trying to understand it. In this way, AI acts as a virtual assistant to both parties.

Self-service purchasing

Customers can go through a self-service purchasing model, such as for opening a new account, while enjoying an intuitive, conversational interface.

AI21Lab’s “largest ever Turing test” found that only one-third of people can tell the difference between AI and human-generated content. Then, on top of that, The Nielsen Norman Group found that people like and can even become attached to large language model AIs.

Software can be so intuitive and personalized that it is experienced as a humanlike interaction, even if the person knows they are speaking with an AI.

Embracing change

Unfortunately, Accenture analysis found that FSIs are lagging well behind the rate of AI and intelligent automation maturity compared to other institutions.

Sixty-two percent of banks surveyed in 2022 think the complexity and risks associated with handling personal data for such projects outweigh the benefits to customer experience.

However, given the direction of travel and the speed of changing customer expectations, it is better to figure these issues out now than miss the boat. The challenges are not insurmountable. You don’t have to do it alone. Specialist cocreators like Modes can help you transform your organization. In so doing, you will pull away from the pack, spurred on by intelligent automation.

Tap us in

If you have a digital project in mind, we’d love to hear about it. Let’s connect on how we can help.

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