The artificial intelligence-based platform was designed to assist bankers in their everyday tasks by offering conversational AI capabilities based on the bank's payment transaction data. The system can provide bankers with useful insights into account and transaction behaviour while leveraging advanced AI and Large Language Models to enable bankers to discover new opportunities and hidden value in their data.
According to PaymentComponents, users don’t require artificial intelligence expertise in order to use the new system. FINaplo.AI includes a chat-assisted dashboard that provides a comprehensive picture of account and transaction behaviour, including trends, patterns, forecasts, and anomalies. The tool was inspired by OpenAI’s ChatGPT, which means that bankers will be able to interact with the system naturally via prompts and questions.
The launch of this new tool comes in the context of the increasing complexity of the financial industry. According to PaymentComponents, FINaplo.AI aims to help financial institutions to navigate this complex environment. In addition, the system can contribute to the protection of customers and the financial system's integrity by detecting and preventing fraudulent activities.
Other potential applications of the new technology include identifying new opportunities, discovering hidden value, and making informed decisions about quality leads, potential fraudulent behaviour, liquidity management, and run-away clients.
According to cointelegraph.com, artificial intelligence is playing an increasingly important role in the financial services industry. Predictive analytics, which can aid financial firms in better understanding and anticipating client demands, preferences and behaviours, is one of the most popular applications of AI, as companies can leverage this information to create goods and services that are tailored to their clients’ needs.
AI-based systems can also be used to improve risk management and fraud detection in the financial services industry, as these solutions can rapidly pinpoint unusual patterns and transactions that can lead to fraud by evaluating large amounts of data in real time. In this case, financial organizations can leverage AI to minimise overall financial risk and prevent fraud-related losses.
Artificial intelligence can also help with portfolio optimisation and financial forecasting. With machine learning algorithms and predictive analytics, financial institutions can improve their portfolios and make more accurate investment decisions. Moreover, financial institutions can rely on machine learning to analyse large data volumes in order to identify trends. Some examples of machine learning use cases include forecasting stock prices, credit risk, and loan defaulters.
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