Numerous banks and financial service providers have successfully implemented Robotic Process Automation (RPA) in recent years. Yet in many of these projects, decision-makers merely realized quick wins. Today, it is becoming increasingly difficult for them to find suitable processes for automation with RPA. The combination of RPA and artificial intelligence (AI) is their opportunity to successfully meet this challenge. RPA and AI then become Intelligent Process Automation (IPA).
It's the combination of RPA and AI that is leading to end-to-end, intelligent automation of finance's many routine processes. RPA can be described as a type of software robot that mimics human actions, but requires precise instructions on where and what specialized data to extract from documents and emails. To achieve this, every RPA activity must be explicitly programmed or scripted.
When data is processed from structured sources, RPA works quickly, accurately and reliably, z. B. When updating customer master data or transferring important content data in financing applications. In this way, it saves employees in banks and financial service providers having to constantly switch between different applications and simplifies routine processes.
But how much data do companies still receive today in a structured form?? Most relevant data resides in unstructured flowing text from emails, chats, and a wide variety of documents and records. This is where RPA fails, because the position and context of the business data is not recognized.
Artificial intelligence, in turn, can also extract data from emails and submitted receipts and deeds – without being explicitly programmed to do so. AI recognizes the meaning of technical terms and independently learns to assign and interpret them correctly. This allows data to be captured and prepared in seconds, even from unstructured and distributed documents from different systems, so that it can be made available to employees in a contextually appropriate manner.
Lower costs, closer to customers with AI
Many banks and insurance companies have recognized this. According to a 2020 AI study by Deloitte, more than 80 percent of executives consider AI to be the decisive competitive factor – especially for topics such as the personalization of financial services and intelligent process automation (IPA) of customer-centric workflows. Financial services providers are capturing mortgage loans and loan applications in a partially or fully automated way, reducing the time it takes to process them by about 40 percent, according to a 2021 analysis by ITyX AG. These are things like card applications, rental citizenships, construction financing applications, or general customer service and back-office correspondence.
But what is the maturity level of the AI solutions currently on offer?? Which services are actually efficient? And how are machine learning (ML) and AI impacting customers, employees and business models in banks? With the so-called "Trend Report AI – Finance", a tool was created together with experts that identifies relevant application fields (use cases) for the use of AI for banks and financial service providers.
One of the essential areas of application for the combination of AI and RPA is the intelligent automation of everyday service processes. The share of routine topics in banking and finance is high. Process automation helps banks and financial service providers to reduce manual tasks to a large extent and thus cut costs while at the same time working in a customer-centric manner. Technological development in this area is well advanced: AI and RPA can be used to automate numerous routine processes such as master data changes and application procedures. Some construction lenders and private banks already capture more than 80 percent of relevant specialized data from submitted mortgage inquiries in an automated way, according to the AI Finance trend report. Especially in low-margin times like today, this is a critical factor in responding faster to requests and pricing more favorably.
Important preliminary stage: Intelligent Document Processing
The basis for the complete automation of routine processes (hyperautomation) is the intelligent capture of specialized data (Intelligent Data Capture). With the help of intelligent document processing (IDP), banks and financial service providers can quickly and precisely extract relevant information from unstructured data.
IDP solutions are now available as transaction-safe services from the cloud and can be deployed additively. This means that no existing system architectures need to be broken up and changed. The software can be easily integrated into existing and future systems. Many software solutions today are also so-called low-code applications. These AI systems are standardized and easily scalable, so financial service providers could deploy them themselves without much IT expertise.
Unstructured data can provide institutions with important insights into the needs of their clientele. AI-based analyses are the basis for automated personalized services. In the medium term, banks will not only reduce their costs, but even more importantly, the intelligent use of NBO (Next Best Offer) and NBA (Next Best Action) will significantly increase their competitiveness by offering their customers a much better customer experience. The principle: AI analyzes a large volume of sample data to generate the right individual offer at the right moment.
What exactly does this look like in practice?? By intelligently combining individual customer characteristics, sociodemographic insights and relevant data from market partners, the technology identifies customer needs and concerns. The combination of AI and ML helps automate large amounts of data and bring together important information from all touchpoints.
Banks and financial service providers thus gain comprehensive customer reports, reliably evaluate performance parameters (KPIs) and specific customer situations, and recognize at which touchpoints the customer journey ends unsuccessfully. For AI to work successfully, it also needs a sufficiently large volume of training data. The use of AI for personalized customer services in banks will lead the way. However, in both technical and legal terms, it is currently still a pipe dream. The financial industry should nevertheless keep an eye on developments in the field of customer analytics.
Chatbots& Using voicebots the right way
The situation is similar with AI chatbots and voicebots. Today, they open up – accepted by more and more customers – the telephone dialog or chat at credit institutions, but that's about it. Although technology is capturing the content of texts and voice messages and interpreting emotions or intentions more and more accurately. But AI-based chatbots and voicebots are still far from being able to do more than short and routine chat and phone dialogues.
There is no doubt that the quality of speech recognition is continuously increasing, but the impact of artificial intelligence on speech automation is a lengthy process. If banks and financial service providers want to use chatbots and voicebots, they should first focus on smooth deployment in simple routine processes.
These are just a few application areas of artificial intelligence that institutions should be aware of. AI will play a role in portfolio and product management – whether in the form of digitized dialog services, algorithmic trading, sentiment analysis, or credit scores. Intelligent algorithms also represent a huge opportunity for better compliance and risk management workflows – from automating application and compliance processes, to fraud detection, to AI support in regulatory affairs.
Intelligent Process Automation (IPA) is transforming the financial sector. While it's hard to automate processes with RPA, the combination of RPA and AI will automate more and more processes in banks in the coming years. Intelligent process automation not only reduces costs, it also creates customer-centric workflows – generating a double benefit for financial service providers. In application areas such as customer service automation and intelligent process automation, the use of AI is already standard today.