The Covid-19 pandemic significantly increased consumer demand online. Most successful retailers offer customers a high quality of online services through different touchpoints. On the contrary, data-driven services in the banking sector are often still hampered by some security concerns or outdated IT architectures.
Yet, data evaluation is the optimal basis for customer-centred offers, innovative products and new business areas. But many decision makers in the banking environment have not yet fully understood the potential of artificial intelligence.
And why would artificial intelligence play an important role in the banking sector?
Artificial intelligence is able to support and automate internal processes that have an impact on customer services. By entrusting the software with the automatic part of the operation it is possible to reduce errors and speed up services. Allowing the right degree of customization.
In addition, artificial intelligence technologies have the potential to improve human decision-making in terms of speed and precision. The economic and productivity benefits are evident.
Artificial intelligence: significant efficiency gains thanks to automated processes
Despite its potential, many banks are not exploiting business ideas that can be achieved with holistic process automation. The latest generation RPA tools allow to free up IT and HR resources, while artificial intelligence deals with the analysis and optimization of processes.
Back-end automation is the prerequisite for an application scenario optimized by artificial intelligence. In addition, smart software tools provide innovative responses to key challenges in the sector. These include customer expectations about service performance, cost pressure in all departments and increasingly complex regulatory requirements.
In daily office work, automation frees employees from tasks that require high time and cost, such as monitoring and documentation of accounting processes or document processing. In this context, a widespread misunderstanding must be clarified: artificial intelligence solutions are not intended to replace employees but to increase their motivation.
For example, GFT has implemented a document processing process supported by artificial intelligence for a large Spanish bank. This involves identifying communications relating to legal disputes, extracting relevant information and determining the subject matter of the case.
The GFT solution has reduced the processing time of these documents by 60%. Previously, this was a manual task that took a long time. When these repetitive tasks are eliminated, more space is left for more demanding but also more fulfilling tasks. So, in addition to the huge cost advantages, the management of processes optimized by artificial intelligence also helps to increase employee satisfaction, reduces the risk of errors and accelerates agility throughout the company.
Increased customer experience quality
In principle, the assessment of business data and customers is not new for banks, but the link of information in real time with data from the past is new in order to apply modern methods of analysis On this basis, credit institutions can make predictions about the likely behaviour of their customers.
Another trend that would be unthinkable without artificial intelligence is the use of voice robots in customer support. Cognitive banking is inaugurating a new era of services for the financial industry: technologies such as the Automatic Speech Recognition or the Natural Language Processing are making personalized communication with customers easier than ever.
The latest generation of chatbots can conduct dialogues in natural language, recognize customer concern, provide the required information or initiate further processes. In addition, digital assistants are able, for example, to remind customers of the contracts that are due and to advise the most suitable products.
For this purpose, the customer’s financial status is analyzed in the background by a query to the database. If necessary, the chatbot organises and coordinates a consultation appointment via video call or affiliate.
But the concepts of artificial intelligence on the front end are not limited to the current account at all. The demand for computer-controlled investment advice is also growing.
Robo-advisers, who develop and implement individual asset accumulation strategies, are becoming a serious competition for traditional securities advice. JP Morgan indicates that artificial intelligence, in the short term, as well as suggesting investment strategies, can, thanks to human intervention, help implement them mechanically and in full autonomy.
The importance of decisions
In this scenario, the real need of the business, even more than the availability of predictive algorithms, is a solution that allows to make decisions that can combine various aspects: artificial intelligence, heuristic rules, explainability.
The concept of explainability, on which GFT has been working for a long time, is of considerable importance when it comes to Artificial Intelligence. In fact, predictive algorithms that make possible the “explainability,” such as the so-called “Clear Box” allow users to understand much more about the decision making process, moving from what a model predicts to how the model predicts. Understanding the reasons behind predictions is very important when you are considering trust in a Machine Learning model: if the business does not trust a model it will not use it.
In this context, for some years it has defined a strategic partnership with RULEX, a company based in Boston and laboratories in Genoa, which has created a platform that, among the various algorithms present, includes one, owner, called Logic Learning Machine, which allows GFT has applied this technology to various areas including insurance frauds, non-Performing loans, the GDPR.
The combination of knowledge and data allows you to verify and test hypotheses, enrich your experience, adapt decisions to a dynamic market and understand emerging trends.
Simple and effective fraud prevention
Artificial intelligence systems also make sense in the fight against cyber criminals: every year, banks in Germany, France, Italy, the United Kingdom and the Netherlands spend a total of more than $135 billion in compliance measures. Although machine learning with model recognition can help in this case, machine-intensive processes with high false positive rates are still found in many credit institutions. Applications to accelerate customer knowledge processes should be on the agenda of all banks With automated artificial intelligence tools, it is possible to verify identity and age within seconds, supplemented by biometric methods. Even anomalies in ongoing operations are detected quickly.
The areas of fraud prevention, anti-abuse and credit scoring are particularly suitable for management by software solutions. For example, calculation models for each customer can be developed, from which it is possible to derive the number and the expected amount of future transactions.
As a result, all activities that deviate significantly from the forecast are automatically reported. In this way, irregularities can be identified at an early stage and the necessary measures can be initiated. Insurance companies can also use smart software to protect themselves from fraud attempts, if the settlement of typical claims is transferred to an artificial intelligence application.
The future belongs to the Augmented Banking
And this is just the beginning: with augmented and virtual reality applications, artificial intelligence will soon reach an even higher level of quality. Lupugmented banking creates conditions for completely new services.
Services that invisibly accompany customers while making online purchases, provide financial products that are suitable in real time and carry out payment transactions. All this through the bank’s infrastructure without the customer having to do anything. It could be several years before this promising scenario becomes a reality.
The use of artificial intelligence is no longer an option for banks, but a decisive factor for success in digital competition.