In 2020, the amount of created and consumed data totaled 59 zettabytes (ZB) This is expected to reach 175 ZB by 2025.
But have you ever wondered how organizing and analyzing this data – even a bucket-load of it – can help the finances industry?
Admittedly, structuring and studying copious amounts of data is a tedious task, making data abundance a curse. Leaving your employees to it would bury them under mundane tasks, slowing processes and leaving little to no room for innovation.
Except, intelligent automation in financial services can help organize and analyze data while automating processes and more.
The result? Better productivity, happy employees, sector innovation, and reduced operational costs.
Ready to learn how to achieve all this? Let’s walk you through using robotic process automation in finance in this soup to nuts guide covering:
- What is intelligent automation (and how is it different from artificial intelligence?)
- Why the financial services industry needs intelligent automation
- Uses of intelligent automation within financial services
Let's dive in.
How to use intelligent automation in financial services
From delivering security codes and handling customer transfers to closing and blocking accounts, intelligent automation in financial services comes with a plateful of efficiency-boosting use cases.
Without it, you’re left with unstructured, unorganized data that you can’t leverage to its full potential. After all, putting humans to organizing data is inefficient and time-consuming. Not to mention, it’s a process prone to human error.
Automation in financial services solves all these concerns and more. It’s no wonder over 90% of financial service firms are reaping positive ROI from intelligent automation.
With that, let’s dig into intelligent automation in finance.
What is intelligent automation (and how is it different from artificial intelligence?)
For a formal definition:
Intelligent automation combines robotic process automation and artificial intelligence to take over manual legwork and transactional, repetitive tasks.
In doing so, intelligent automation, also known as intelligent process automation, helps:
- Boost productivity
- Reduce operational costs
- Increase employee satisfaction and
- Improve customer experience
Put simply, intelligent automation is what its name suggests – using automation intelligently to drive business efficiency and innovation.
Think of it like this: when employees aren’t tied to mundane, manual labor, they’ve more time and mental bandwidth for qualitative work and creativity. Naturally, this makes room for business innovation.
On the other hand, with IA automating workflows and data organization, and assisting with decision-making, productivity blooms. What’s more, there’s less scope for human error. Such improved efficiency naturally reduces operational costs.
AI vs IA: What is the difference between AI and IA?
Where artificial intelligence (AI) emulates human cognitive functions, intelligent process automation wears an assistive hat. How? By working with humans to encourage better decision-making.
Put this way, you’ll see that intelligent automation overcomes the limitations of artificial intelligence. This is further confirmed by IA’s multiple names that use words denoting its complementary nature:
- Assistive intelligence
- Cognitive augmentation
- Intelligence amplification
- Machine augmented intelligence
Another key difference is that while both terms are sometimes used interchangeably, artificial intelligence is a broader term than intelligent automation.
In that, it covers the concept of using machines that perform human-like tasks – only better (in terms of efficiency – this doesn’t include emotional intelligence and social skills). Generally, you’d label these tasks as “smart” or “intelligent.”
For example, think Siri and self-driving cars.
On the flip side, intelligence amplification brings together technology and humans to augment human functions.
Chatbots are a simple yet effective example here. Trained and managed by humans, they are used to improve customer services significantly.
Let’s sum these differences below:
Why the financial services industry needs intelligent automation
The financial services sector showcases two ripe reasons that set the stage for finance automation:
- One, the industry grapples with mounds of unstructured data. This data needs to be unlocked, managed, and leveraged for efficiency and better decision-making at scale.
- Two, the data needs to be presented to stakeholders and clients. Again, organized data here will help automate customer requests effectively.
With augmented intelligence, the financial sector can structure and use data consistently, accurately, and efficiently.
Further, by integrating automation and data, IA can help banks and financial firms including insurance and investment companies use their existing data bank for large-scale data analysis.
This, in turn, automates operations and workflows while aiding data-informed decision-making. For example, financial analysts can use the data to predict outcomes such as quarterly earnings.
Besides these broad reasons to leverage unstructured data for unlocking opportunities, here are more reasons why robotic process automation in finance is needed:
- Uncapped operational costs
About one-third of community banks spend upwards of 5% on compliance. According to the same report, only 1.8% expect their compliance costs to decline by at least 5%. Plus, 15.9% say compliance regulation is among the greatest challenges that banks faced in 2021.
Such rising operational expenses paired with increasing regulatory requirements impact banks’ performance.
- Suboptimal client experiences
Operational costs and employees focusing on mundane, day-to-day tasks culminate in client experience taking the backseat. No wonder, customers who say they’re sure of leaving their bank blame poor customer service as their primary reason. 56% of those who have left say the bank could have changed their mind.
- Increased use of online banking
The COVID-19 pandemic grew online banking with over a third of customers using it. Credit card giant, Mastercard also reported a 40% jump in contactless transactions worldwide in the first quarter of 2021.
Meaning: customer demands from financial services are drastically changing as are their expectations related to the experience provided.
- Uncontrolled money laundering
Money laundering contributes to a 2-5% loss to the global GDP. In 2020 alone, banks paid $10.4bn in fines for money-laundering violations. Naturally, this confirms the response to financial crime so far has been mostly ineffective and expensive.
Fortunately, artificial intelligence in financial services can help with all this and more – as you’ll learn in the next section.
Uses of intelligent automation within financial services
So far, you’ve learned financial institutions need intelligent automation to:
- Uncover previously unrealized opportunities
- Improve customer experience
- Reduce operating errors
- Increase efficiency
- Boost revenue
- Lower costs
The industry also needs automation to cut back the time going into task handling and marketing new services and products.
The question now is: how does robotic process automation and artificial intelligence in financial services help?
Let’s explore that below:
1. Real-time transaction analysis and risk monitoring
It’s never easy – even humanly possible – to analyze large amounts of data quickly. Intelligent automation can manage all data including unstructured data at a fast pace minus the odds of error.
As a result, the finance industry can use AI to analyze data showing the history of risk cases to identify symptoms of potential issues before they grow.
AI can also monitor real-time activities in a specified environment and market. Consequently, it helps make accurate projections and creates reports with detailed forecasts based on several variables – assisting in growth planning and more.
2. Identifying money laundering
Laborious manual processes involved in monitoring money laundering make fighting fincrime an inefficient and costly process.
Robotic process automation in finance, however, can help. By collecting customer and counterparty data information from various sources (think internal sources and external sites), IA can alert on potential AML transactions.
Not only does this speed things up but it also improves efficiency in catching money laundering cases. Take American Express, for instance. They use fraud algorithms to supervise all transactions in real-time.
The result? The credit card giant has improved:
- Fraud detection accuracy. Up to 6% improvement in particular segments.
- Speed. The IA-fueled system yields a 50x improvement over a CPU-based configuration.
Want another example? We have BNY Mellon. Using intelligent automation, the investment banking company built a fraud detection framework that has improved accuracy in fraud detection by 20%.
Considering this efficiency, it makes sense that fraud detection is ranked among the top three use cases for AI among fintech, investment firms, banks, and other financial institutions.
3. Finding best-fit financial products for customers and optimizing sales and marketing
According to the same survey cited above, portfolio, sales, and marketing optimization are also among the leading use cases of IA.
At the Royal Bank of Canada, for example, a private AI cloud analyzes data within a fraction of a second. In doing so, it has helped reduce client call rates. To boot, the AI cloud assists in delivering faster applications for the bank’s clients. All this boosts customer experience significantly.
What’s more, smart chatbots offer clients self-help solutions in the banking sector. This reduces wait time and lessens call operators’ workload too.
Similarly, using artificial intelligence in financial services offers efficient and customer experience-boosting tools that allow them to:
- Check their balance
- Schedule payments
- Look up account activity
In terms of sales optimization, credit scoring with IA helps in contrast with the traditional systems. Consequently, lenders can tell between high-risk applicants and credit-worthy candidates that don’t have an extensive credit history.
Such a system is also objective. Using the software in loan-issue apps, for example, is a great way to evaluate eligibility for loans.
4. Offering personalized banking
AI-powered apps also offer personalized banking experiences such as:
- Income tracking
- Personalizing financial advice
- Detecting spending habits and recurring expenses
- Optimizing plan for finances management and financial tips
Chase and Wells Fargo among other banks use such mobile banking applications for bill payment reminders, offering personalized expenses plans, and more.
5. Providing algorithmic trading
Algorithm trading uses automated technology to analyze large data volumes for executing trades quickly and effectively. It can help:
- Make quick and accurate trade decisions based on market evaluations
- Identify trends and patterns in market price change
In short, with algorithm trading, you can achieve what you can’t achieve by using human intelligence only.
In fact, the chief aim of algo trading is to assist in maximizing market returns for your investment funds. In doing so, it helps:
- Execute trades faster than humans
- Increase the odds of successful investments
- Reduce the cost of manual work that goes into processing bulk traders
Take Alpaca as a case in point. It pairs high-speed data storage with deep learning to provide forecasting options – both short and long-term.
6. Using conversational bots for pricing and service requests
Customer service and support involve high operating costs and extensive manual work. As a result, the departments see high caller wait time – often resulting in frustrated people airing their grief on social media.
To add, the banking industry’s customer service department is tied to compliance-related regulations. Naturally, this adds work to reps’ plates.
Conversational bots offer a helpful – even customer experience boosting solution. American Express, Well Fargo, and Bank of America are some names using chatbots for answering FAQs to fetch information customers ask for faster than agents.
Here’s more on how bots help:
- Reduce call-handling time, human errors, and duplication caused during manual input of call information.
- Cut down applications managed per agent and take on back-end processing burden from service reps.
- Identify factors for improving service quality and customer satisfaction based on the available data.
Looking for a real-life example? HDFC bank uses a virtual assistant (EVA) that has successfully handled more than 2.7 million customer queries within six months. To boot, the bot showcases an accuracy of over 85%.
7. Scanning and processing documents
Intelligent automation involving machine vision and natural language processing (NPL) can help analyze all text-based documents. These include:
- Legal and regulatory documents
- Social media comments
- Survey responses
- Online reviews
NLP helps with smart document search as well, assisting in finding required information from a wide pool of scanned documents.
In short, use IA to extract any key information from large text volumes, reduce manual work, gain insights, and more.
JP Morgan Chase’s COIN leverages NLP for this purpose. The Contract Intelligence (COIN) software helps the legal team scan and review high volumes of legal documents. The result? COIN saves 360,000 hours per year for the bank’s legal team.
8. Investment analysis
Financial services can use NLP to study the competitive landscape and evaluate investors.
By evaluating environmental, social, and governance (ESG) ratings, investors can determine who to invest in. In that case, a high ESG rating is a telltale sign of better profitability for an investor.
On the other hand, firms looking for investors can use IA to their benefit too. Together with NLP and sentiment analysis, they can compute their and their competitors’ ESG ratings. This helps them understand their organization’s standing and their status as compared to others.
9. Humanoid robots in bank branches
Robots in bank branches offer another way how intelligent automation in financial services can boost customer experience.
HSBC’s humanoid robot, Pepper, for instance, helps with this:
- Educate customers on product information
- Make self-service available to them
- Improve customer engagement
Most of all, it only makes relevant product suggestions to customers by first asking them questions to determine their needs.
Want numbers to understand the robot’s benefits? According to data HSBC shared in 2020, Pepper has helped increase new business. This is due to the 25,000 customer interactions it has had.
10. Automating processes
IA’s use in process automation is extensive.
Simply using intelligent character recognition can automate a boatload of extensively manual, time-consuming tasks that occupy several work hours.
Similarly, using finance reporting automation with AI-enabled software, you can get help with data verification and report making. From there, artificial intelligence takes over data extraction from forms and agreements, document reviews, and more.
By automating repetitive tasks, Ernst & Young has reported a 50%-70% cost reduction that goes into doing these manual tasks.
Speaking of specific use cases, let’s look at wealth managers. They can leverage automation finance to reduce manual work involved in setting up client accounts.
Traditionally, this process is packed with a lot of paperwork and tons of exchanges between wealth managers and clients. However, streamlining the process using a combined trio of digital forms, automation, and Optical Character Recognition (OCR) helps:
- Dial down the set-up time
- Reduce room for human error
- Drastically improve customer experience
Similarly, asset managers can automate processes and reduce risk in operations.
Firms, for example, heavily rely on spreadsheets in addition to their main order management platform. Of course, this slows processes and comes with high risks of human error in managing orders and implementing new strategies.
All this is solvable with the help of intelligent automation (read: digital forms and RPA) though.
11. Processing insurance claims
Here, IA helps in several ways including:
- Helping insurance providers identify fraudulent claims
- Providing relevant insurance policies suggestions to customers
- Streamlining insurance providers work process – making it efficient
By embracing digital workflows, the insurance industry can also take an analytics-driven approach. This, in turn, can help them understand their target audience better and create new customer satisfaction-boosting digital services.
The future of intelligent automation in financial services
Despite its wide applications, intelligent automation in financial services still faces numerous challenges that stunt its growth.
- Lack of IT support. Intelligent automation requires support and collaboration from IT teams skilled in Cloud operations.
- Lack of employee support. Implementing any new system or process requires human support – an element that can’t be overlooked when introducing IA in financial services
- Unclear vision. Most businesses are taking a piecemeal approach to implement AI. But a clear strategy is essential to taste its full benefits.
- Fragmented processes. Processes at companies are fragmented by departments. Intelligent automation, on the other hand, involves automating several processes across departments and teams.
Even so, the future for intelligent automation in financial services is bright. Moving forward, two things, in particular, can make implementing and scaling IA easy:
- Combining different technologies to automate complex processes
- Using low-code tech that doesn’t require coding and programming skills
So how do you want to start using intelligent automation?