Artificial Intelligence for the digital transformation of Financial Services

Providing value to your clients has always been at the heart of all digital transformation pathways, and Financial Services is no exception.

Creating a new way to interact with money and investments, both for consumer and business clients, primarily consisted, until very recently, in digitizing the back end processes, therefore optimizing and simplifying time-consuming activities and reducing costs significantly.

But today, and for the future, the challenge of the banking world and the financial sector is investing more and more in customer satisfaction and in the personalization of products and services, moving the focus of those who have to invest in new technologies to the front end.

Artificial Intelligence projects, along with Machine Learning and RPA (Robot Process Automation), follow the same trend, moving ever increasingly on the relational aspect and on providing added value to customer requirements.

Artificial intelligence provides a way of satisfying the requirements of clients who are looking for more intelligent, more economical and more secure ways to access, spend, save and invest their money.

The protagonists and beneficiaries of the use of new techniques, from Natural Language Processing to Predictive Analytics, are not just clients but rather all stakeholders, from research institutes, universities and Fintech companies to the employees themselves, who must “systematize” in order to answer and anticipate the needs of the end user, in a context of extremely fast technological evolution.

The need for a “holistic” artificial intelligence strategy which extends to banks’ lines of business, to the usable data, to partnerships with external partners and qualified employees, is ever-increasingly key for the future of companies in the world of finance & banking.

The digitization of financial services, in a trend which is seeing ever-increasing reductions in the number of branches and their opening hours, inevitably produces a quantity of data which must be managed both in the front end and back end environment, and which feed the AI and machine-learning algorithms which are crucial in the evolution of the processes in play.

While on the one hand investment banking operations are based on Machine Learning to fine-tune algorithms and forecasting models for quantifying and reducing risk, therefore taking “informed” and safer decisions, in retail banking predictive analysis is used to find new information which can help improve customer loyalty and create a customer experience which is initially more and more omni-channel in nature, before getting them used to moving more and more from the physical channel to the digital one.

Low-added-value activities which used to keep teams occupied can now be automated thanks to AI and RPA, increasing the productivity and efficiency of many processes, reducing human errors and allowing staff resources to dedicate themselves to more strategic activities that machines are not capable of.

But what are the areas of application of AI in financial services?

AI applied to credit checks to reduce the risk of insolvency

Artificial intelligence uses more complex credit assessment approaches compared to traditional systems, so that banks can understand whether someone is a high-risk applicant or simply does not have sufficient credit history.

AI and Trading: monitoring and optimizing investment choices and tailoring them to each individual client

Trading systems based on artificial intelligence can analyze enormous quantities of data more quickly than people can. The high data processing speed leads to fast decisions and transactions, allowing traders to make greater profits in the same period of time; thanks to predictions made by the artificial intelligence algorithms, they are more accurate because they can analyze a lot of historical data.

Risk Management and Predictive Analytics

AI offers incredible processing potential and can manage enormous quantities of both structured and unstructured data, while machine learning algorithms can also analyze the risk history and detect any signs of potential problems before they occur.

Fraud Prevention and Cyberattacks

Security of personal data and access credentials is a key aspect both when choosing a bank and when changing banks. On the other hand, the trust relationship between the user and the bank is a two-way street, and companies in the finance sector must also protect themselves from fraud and money-laundering attempts.

Cybersecurity in the finance world is increasingly put at risk by new threats, but solutions based on artificial intelligence can use machine learning to rapidly respond to hackers’ strategies. Fraud-detection tools based on artificial intelligence can analyze the behavior of clients, track their positions, and determine their purchasing habits in order to automatically detect anomalies and create alerts.

As regards the fight against money laundering, machine-learning algorithms can rapidly detect suspicious activity and minimize the cost of investigating money-laundering schemes thanks to identification of specific patterns.

AI and Customer services in Financial Services

Solutions based on AI such as chat bots and virtual assistants provide personalized financial tips 24/7 thanks to Natural language processing logic and logic which keeps track of income, recurring essential expenses, and spending habits, providing an individual optimized plan.

These solutions, as well as allowing customers to make guided and informed choices, which minimize risks, allow users to be guided towards ever-more digitized interactions with their bank, thanks to the unparalleled advantage of time savings.

Ticket management is based on the same principle, allowing the classification and management of large quantities of support tickets in an optimized manner, on the basis of priority and customer satisfaction.

A customer-centric approach which allows their needs to be anticipated and creates the basis for long-term loyalty.