AI automation entered the Banking Industry quietly with only automating traditional and simple jobs like data entries, cash deposit, passbook updating and salary uploads to name a few. Nowadays AI automation is pushing the boundaries of technology by implementing robotics to optimise almost every process from selling financial products to customers, to loan processing; creating much more productive relationship between customers and machines.

The Banking industry is in an ideal position to harness AI automation systems to meet ever-growing regulatory demands. Nowadays the biggest banks execute AI to make vast changes in work force, customer experience and expenses. In fact according to Accenture 2016 survey held with bank executives, when asked in which areas they plan to increase their use of automation, their top three responses were customer interaction/ experience (90%), IT tasks (90%) and knowledge worker tasks (91%). So let’s see these areas in practice and also take a look at the other popular uses of AI automation in the Banking Industry.

Real-Time Data Processing

One of the biggest challenges in Banking is having to process, control and analyse millions of unstructured and fluctuant data. AI automation, not only processes big data simultaneously; it also leaves no room for human errors and doesn’t let anything drop beneath the radar. Real-time data processing is mostly used in the cases below;

Contract, compliance and regulation intelligence : The banking industry spends most of its operating costs, on the handling of compliance and regulation, which is a big challenge when it needs to be done in real-time. Therefore big banks use AI automation such as JP Morgan. They developed a program called COIN, which enables them to decrease their number of loan-servicing mistakes. According to the program’s developers, these mistakes stemmed from human error in interpreting 12,000 new wholesale contracts every year.

: The banking industry spends most of its operating costs, on the handling of compliance and regulation, which is a big challenge when it needs to be done in real-time. Therefore big banks use AI automation such as JP Morgan. They developed a program called COIN, which enables them to decrease their number of loan-servicing mistakes. According to the program’s developers, these mistakes stemmed from human error in interpreting 12,000 new wholesale contracts every year. Risk management: Machines are able to read all the data inputs that differentiate good transactions from fraud simultaneously, which can be easily bypassed by humans.

Machines are able to read all the data inputs that differentiate good transactions from fraud simultaneously, which can be easily bypassed by humans. Trade monitoring: AI algorithms can also detect and investigate rogue trading where authorities might be deceived on several occasions for a long period of time.

AI algorithms can also detect and investigate rogue trading where authorities might be deceived on several occasions for a long period of time. Intelligent cash management: By applying AI automation to cash inflows and outflows, machines can learn cash collection cycles and recommend a discount within a pricing strategy to increase cash for different businesses. According to CenterState, a business could increase its cash balances by an average of 15% and probably closer to 30%.

Greater Efficiency

AI automation is a great way to reduce the time and effort spent on routine, frequent and repetitive tasks (such as analyzing status reports, tracking compliance and regulations, handling reconciliation and IT tasks such as software updates, password resets and troubleshooting etc.), which are the biggest operational workloads in the banking industry.

Since Machines are not only used in simple tasks but also started to determine giving loans, or recommending investments to customers; recently, a common concern in the industry seems to be ‘robots are going to take all the jobs’. In fact, an Accentura survey ‘Benefits of Robotics in Financial Services’ indicates that in some areas in the Banking industry, time to perform tasks was reduced by up to 90%. Also JP Morgan Chase & Co has managed to cut time spent on mundane tasks such as interpreting loan agreements down to literally seconds rather than a total of 360,000 hours a year, using machine learning.

Yes, automating speeds-up the tasks and enable banks to optimize their efficiency in a very obvious way, however it will also direct employees to higher-value tasks and more strategic activities. So, there is no need to panic (at least for a couple of years)!

Cost savings

There is no doubt Banks know how to handle money and it is not a surprise to hear that the banking industry is one of the first to utilise the latest innovations when it comes to cost savings.

As aforementioned, AI automation increases efficiency, speeds up the time spent and eliminates human error therefore banks manage to make a huge difference on cost savings… How huge?

Let’s first take a look at IT departments, which Banks have been using AI automation for a long time. In an Accenture Technology Vision survey, nearly half of the banks indicated that they have achieved 15% or more in cost savings from automating systems in the past two years. In some financial services areas, costs were reduced by 80% and time to perform tasks was reduced by up to 90%.

If the percentages are not enough for you to visualise, a Citibank report* might be more meaningful: Citigroup states that handling compliance and regulation costs the banking industry $270billion a year and accounts for 10% of operating costs. So, the banks have to either double the number of employees to handle compliance and regulation, or use AI automation.

Better Customer Experience

In this customer-oriented world, customers’ diverse needs, priorities and preferences are forcing banks to redefine how they interact with them to offer the most relevant services, whenever they want. AI algorithms now are able to track, analyse customer data (behavioural, demographics, location etc.) to determine the customer identity and in addition to recommending the best service, they are able to understand and solve a problem immediately. Not to mention providing lower-cost services.

One of the areas where AI automation started to blossom for customer services is investment advisory. Roboadvisors are playing a growing role in wealth management, with their knowledge and ability to learn current market conditions and more importantly evolving customer goals. In addition to these, they are creating customer value through cross-selling complementary services.

Machines are also able to recognise expressions and emotions and build relationships with customers like chatbot softwares. They can understand and learn from interacting with people, which enable them to empathise with customers and ultimately resolve complex issues.

Swedbank is incorporating this kind of technology and they launched an AI bot (virtual personal assistant) named Nina. It was created to provide a tool that can identify the root cause of a problem and answer questions automatically. This way Swedbank managed to reduce visits to branches and calls to the contact center.

Picture

Proactive behaviour is the real key feature of chatbots. Customers can have conversations in natural language, and chatbots can reply when they have something important to tell. While chatbots are providing customer service management, understand a problem and give recommendations, they are not developed enough to provide services fully unassisted yet.

Improvements on AI automation tools will better identify consumer preferences, react with insight and emotional intelligence soon. When added the increased speed; these improvements will definitely build more meaningful relationships with customers, maybe better than humans.

New Working Areas

As we have been mentioning that machines are better than humans in most of the tasks in the banking industry, the ultimate question enters our heads: is this the end of most of the jobs? Yes it will be the end of some jobs however also new roles will be redefined. AI automation will not only change the way bank employees work, but also the type of work they do.

Let’s look at this way; when internet banking and mobile banking started to take the majority of customer interactions, the number of branch employees have been decreased. But also, new job titles and categories have significantly emerged- mostly in IT departments.

http://www.caci.co.uk/

The same logic goes with AI automation. It replaces some portions of the jobs while creating new jobs. For example, investing in mid- to low-range net worth customers with a personal touch wasn’t profitable for the banks before. However with AI automation taking most of the load from employees, banks can now create personalised new services to these clients which means there will be more clients, more services for each individual client and therefore jobs for employees.

Moreover; because all devices and systems collect different types of data, not all AI systems will have the same data to work with. Therefore banks have to employ more people to overcome similar challenges.

AI automation will definitely create a digitally empowered workforce and enable employees to focus on higher-value functions and more strategic work, such as developing new product offerings, providing a highly tailored customer service for improved business outcomes.

HR departments will play a very critical role in here by determining the workforce, managing skills development and talent sourcing.

Conclusion

The successful banks of tomorrow will be those who systematically integrate human intelligence and machine intelligence and using their coexistence in their products, services and business models. AI automation will continue to add value to employee tasks, deliver better experience and cheaper services to clients and return stronger value to shareholders’ investments.

* https://www.ft.com/content/3da058a0-e268-11e6-8405-9e5580d6e5fb