Silvia Speranza, Regional Vice President Appian Italiareveals the three main technological trends that will characterize the banking sector and to monitor in 2024.
The combination of high interest rates, high inflation and greater economic uncertainty has meant that the financial services sector will have a major focus on these critical issues in 2023. For this reason, many companies are placing an even greater focus on improving cost effectiveness operational, reduce regulatory risks and keep customers satisfied through excellent service.
In this sense, technological advances will have a great impact on the future of banks and the financial sector as a whole: institutions that adopt artificial intelligence (AI) and other advanced technologies will certainly be able to adapt to changes more easily than those who consider them optional.
Specifically, here are the three main technological trends in the banking sector that will characterize 2024, as also emerged in the latest edition of the Sibos conference.
AI will continue to change banking technology in the coming years
It may seem that artificial intelligence has become the topic of the moment, but this is not a temporary trend. AI has the power to revolutionize banking across risk management, operational efficiency, customer experience and more. Digital transformation through an effective AI strategy will provide financial services companies with the ability to become more agile in the face of changing macroeconomic landscapes.
Here, in particular, are some use cases of AI in the financial services sector:
• Regulatory risk and compliance: Artificial intelligence can identify patterns and behaviors to identify risks early. By analyzing historical data and predicting future scenarios, banks can assess operational, market, and credit risk and make their risk mitigation efforts more effective.
• Customer care: Customer satisfaction and loyalty in the banking sector are extremely important. When you combine AI technologies like chatbots with employees working to solve critical customer problems, you are able to improve outcomes and better engage users through personalized experiences. Furthermore, the use of AI for customer care allows you to further analyze data on customer behavior, improving service offerings and marketing activities.
• Operational efficiency: AI can automate routine tasks to save time and improve operational efficiency. It has the ability to analyze data and information more quickly and accurately than human operators, improving transparency within an organization and enabling business leaders to make better, faster decisions.
Data is the core and foundation for making the most of AI
One of the main trends emerging in discussions is the focus on data. Many traditional banks and financial institutions still use manually created and managed spreadsheets, which increases the margin for error and risk.
Connect data
Isolated data leads to a limited perspective and incomplete vision; where possible, you need to connect data between different systems to create a unified view and exploit its full potential. This not only improves AI automation processes, but also ensures that everyone who needs to access data within the organization has the most accurate information. For large banks this is a challenge and technology that uses data fabric can help in this sense. A data fabric allows you to work with data in a virtual structure, without having to migrate it from one platform to another to use it. With a data fabric it is as if all the data is connected, regardless of where it is located.
Maintain the integrity of your data
If the data is not of good quality – that is, as complete and accurate as possible – the technology based on it simply will not work. Poor quality data can also lead to poor business decisions, regulatory fines and customer dissatisfaction. To improve its accuracy, IT teams must be involved in the definition, standardization and management process. You need to look for pain points in your data ingestion processes and work to improve these workflows to improve the integrity of your data sets.
Stay up to date on the critical issues of AI and data privacy
Business leaders have legitimate privacy concerns when it comes to data and AI. The information entered in the LLM (Large Language Model) of many AI systems are used to create models for the future. If you provide confidential information or sensitive customer data, that information may become public, creating additional proprietary and regulatory risks for businesses.
The solution to this problem in the financial sector is private AI. With the’Private AI, the language model is internal to your company and is trained only on your data. This way you enjoy all the guaranteed benefits of AI, while maintaining a high level of security for your company and customers. Additionally, AI results specifically reflect your customer base, allowing you to learn more about their needs and habits.
Digital assets are on the rise; automation can help
Most banks and financial organizations that manage large assets are exploring the digital assetsthe tokenization and technology blockchain. The digitization of these assets will bring access to more potential customers and move money around the world more securely.
A growing number of individuals are interested in investing in these new wealth management assets, but traditional business models do not always make this possible. Fintech companies and modern banks must ‘address pain points and solve data challenges.
How do the most advanced banks act? With AI automation. Many of the tasks associated with digital assets can be facilitated by automation, such as asset value assessment, financial forecasting, and more. AI can also be used for risk assessment and management and checking regulatory compliance of these financial products.
The case of the BCC ICCREA Group
A concrete example of how Artificial Intelligence, data fabric and process automation are already contributing to a radical transformation of the most dynamic banking institutions is that of the BCC ICCREA Group, which has chosen Appian to undertake a path of digitalization and automation of the main processes. With the aim of improving customer assistance, faster delivery of services and reducing the complexity and manual activities associated with regulatory compliance, the Banking Group which today brings together 116 federated institutions, has adopted the Appian platform to ensure a rapid and continuous improvement of processes through the monitoring of the main performance indicators and a more effective re-engineering of the methods of providing services.
To date, around thirty applications of different complexities have been created with Appian. For example, all compliance processes (risk assessment, planning, execution and monitoring of checks) and an anti-money laundering application that uses AI (NLP/NLU) and in which Appian technology connects the different embedded systems. Drawing on web sources and digitized archives of paper sources, the solution analyzes over 2,200 national and local newspapers to find any adverse news regarding a bank customer.
Il BCC ICCREA Group also adopted a solution of machine learning for the management of potentially suspicious banking operations: the algorithm learns a customer’s behavior patterns and recognizes potentially suspicious deviations. This solution dramatically reduces false positives and assigns a priority score to events that actually require verification.
The Appian platform then made it possible to create an application for Pog (Product, Oversight and Governance)a Bank of Italy regulation according to which the life cycle of banking products must be traced, inspected and monitored.