Understand exceptions, to get fewer payment rejections
Know the clients close to fraudsters to act faster
Predict amount & volumes to plan well ahead
Reveal the purpose of every payment
Identify influential customers to expand your market
Predict runaway customers to convert them to loyal
You feed your messages through a secure and compliant process
The AI models trained on financial messages will analyse your sanitised data
You will make informed decisions based on data you trust
I will analyze your financial messages and create the network representation of these accounts and transactions using ML graph analysis, providing actionable insights to your Sales team. This will allow them to identify quality leads and run-away clients quickly, and keep your business on the right track.
Let’s boost your sales and grow your business! I will analyze your financial messages and group accounts using NLP and ML clustering techniques. Then your Marketing & Sales teams can put into service my findings and recommendations. My Conversational AI will be the teammate that will help them target clients with similar behavior and increase cross-selling and upsell opportunities.
I will analyze your financial messages and identify anomalies based on multivariate transaction analysis, providing alerts to your Risk team. This will allow them to keep a closer eye on transactions and accounts, and quickly spot any unusual or suspicious activity.
Let’s make sure your finances stay on track! I’ll use my skills in multivariate regression analysis to analyze your financial messages and predict any future currency needs. Then, my findings and suggestions will be available to your Treasury team to make informed decisions when the rates are most favorable.
FINaplo.ai is a SaaS AI solution for the Banking Industry. AI adoption is no longer just a trend for neobanks; all Banks, Corporations, Fintechs, and Financial Institutions that exchange financial messages can benefit from its valuable insights and predictive analytics. With FINaplo.ai, even traditional banks with no AI experts in their payroll can leverage AI technologies to gain a competitive edge in the banking sector.
The banking industry is a vast territory with many complicated rules and challenges to overcome. Compliance for fraud detection and money laundering will be more difficult now that transactions are processed in real-time. Revenue concerns because new and old competitors are using emerging technologies to lure your customers. Insecurity and decision fatigue because of all the unknown parameters that influence the day-to-day banking services. This is why FINaplo.ai was designed to provide useful knowledge to multiple departments like Sales & Marketing, Operations, Treasury, Payments, and Risk.
Through Artificial Intelligence and Machine Learning Finaplo.ai analyzes processes exclusively financial messages, such as SWIFT MT, ISO20022, and payment schemes such as Credit Transfers, Direct Debits, and Instant Payments. This subject matter expertise differentiates Finaplo.ai from generic AI tools and ultimately saves banks from bottlenecking in Payments and Risk. And that’s because the PaymentComponents expert team is dedicated to financial services and especially on financial messages. You bring the data, and Finaplo.ai brings all the rest.
Financial messages encompass a great deal of valuable information. They contain cryptic and proprietary information such as multiple involved parties, intermediaries, charges, rules, and instructions. Careful analysis with advanced machine learning techniques can reveal hidden customer behavioral patterns, unknown relationships between customer accounts, valuable information on the purpose of payments, and much more. This is huge business value for Banks and Financial Institutions and not just for fraud detection.
Finaplo.ai can identify unusual customer behavior in payment flows, analyze payment instructions to reveal the exact reason for payment, highlight influential customer accounts, aggregate and filter customer accounts according to common attributes, and present numerous predictive analytics along with many other valuable insights. Its Conversational AI will be your new helpful teammate.
You and anyone in your organization invited as a user of FINaplo AI service will be able to ask questions and get intelligent insights using its Conversational AI. Or see augmented analytics and AI/ML-powered insights through a rich dashboard with comprehensible explanations. You can also download the processed data in .CSV format to add them to your bank's internal tools.
Before any of your data is uploaded to Finaplo.ai, a sanitization process will precede in your own secure environment. During this process, any data containing sensitive information (IBANs, Names, etc.) will be excluded or obfuscated. Then the sanitized data will be fed to the FINaplo AI models that are hosted on Amazon Web Services (AWS), which is known for its robust privacy and security measures. You will then work in a secure environment with strict permissions that will protect your data against any unauthorized access. You will retain ownership of your data in a private, dedicated, isolated cloud environment that only you can access and manage in terms of data and user permissions.
The pricing structure for Finaplo.ai is fair and simple. You pay a single fee based on the volume of financial messages you upload, and there are no additional processing fees when you view your charts and insights.
Finaplo.ai uses financial messages in its core analysis. Yet we know more data equals more valuable insights. This is why FINaplo.ai was designed to be a flexible ai system that allows data stitching and enrichment typically through a custom project.
Historical data are uploaded in bulk files. The module we provide will first sanitize them and then upload them to your Finaplo.ai private space. This is for SWIFT or ISO2022 message files. Real-time messages or transaction are fed using a provided API.
Yes, with the ability to analyze large amounts of data quickly and accurately, AI can assist financial institutions with a wide range of financial tasks such as trading, risk analysis, fraud detection, budgeting, forecasting, tax preparation, financial reporting, loan processing, and credit scoring. By consulting predictive analytics and automating these processes, banks can increase efficiency, reduce costs, and minimize errors. Additionally, the use of Generative AI can also create new, previously unseen financial insights and solutions using natural language processing models. However, it's important to note that Artificial Intelligence should not replace human decision-making entirely, as some financial decisions require human judgment and expertise. Instead, AI can be used as a powerful tool to augment human decision-making, minimize risk and enhance the overall financial process.
AI is not based on fixed rules but instead operates on a technique that learns from data, enabling it to provide valuable insights to assist individuals in making informed decisions. This way AI is being utilized in the banking industry for tasks such as fraud detection, customer service, and loan underwriting. These banking-specific AI solutions can analyze transaction data to identify fraudulent activity, provide personalized recommendations and assistance to customers, and analyze data on potential borrowers to inform lending decisions. The next big thing could be using Large Language Models (LLM) in AI applications in the banking sector but there are other AI technologies and techniques in use today.
There are several potential benefits of AI adoption in the banking industry, including the automation of tedious or time-consuming tasks, the optimization of processes and decision-making, and the ability to analyze and interpret large amounts of data. Artificial Intelligence has the potential to improve the efficiency and effectiveness of financial institutions, resulting in better services to their customers.
The key difference between conventional programming and machine learning (ML) in banking is that programming relies on explicit instructions written by a programmer, while ML learns from data and identifies patterns to make decisions. In banking, programming is often used to automate routine tasks like customer authentication or transaction processing, where the rules and processes are well-defined. On the other hand, ML because of its AI capabilities, is used in banking for fraud detection, risk prediction, and improving customer experience. ML algorithms can analyze vast amounts of data to identify patterns that humans may not be able to detect, allowing for more accurate predictions and decisions. While programming is useful and irreplaceable in specific contexts, AI and machine learning have the potential to significantly transform the banking industry.