Plat.AI offers banks and financial institutions real-time predictive modeling to help make accurate financial decisions, reduce friction costs, and improve productivity and profitability.
Plat.AI's predictive analytics software uses an advanced machine learning algorithm to analyze data sources in real-time. It detects patterns from the data, predicts and prevents financial risks, and improves customer retention.
It extracts meaningful insights from raw data sets to provide accurate real-time decisions, which financial institutions can use to solve complex problems unique to the banking and finance sector. These include but are not limited to assessing risks, managing assets, and dealing with fraud detection.
At Plat.AI, we work with banks and financial institutions to deliver custom AI and machine learning models to help improve revenue, detect fraud, and reduce financial risks. Our data scientists are ready to build, deploy, and maintain a real-time decision-making engine with your data.
Keeping up with new technology is vital for financial institutions to:
Below are trends for the financial services industry:
Thanks to machine learning and specialized algorithms, AI can analyze customers' transaction history and behavior and predict future actions accordingly. As a result, Plat.AI can help financial institutions detect suspicious behavior.
One of the most useful applications for AI in finance is making forecasts with real-time data. This can help improve loan underwriting and reduce financial risks.
Plat.AI provides the necessary tools to overcome challenges inherent to big data. Here are just a few examples:
Financial institutions compete to provide clients with the most innovative services. Plat.AI can help power a personalized and optimized customer experience. Our AI solutions can also assist financial companies in reducing client turnover.
Financial institutions have large datasets about their customers, which they can use to offer a fast customer service experience. With Plat.AI, banks can incorporate chatbots and virtual assistants into their services and provide fast, unique, and effective customer service across their websites and mobile applications.
AI-driven solutions can automate regulatory compliance obligations, such as reporting to the Office of the Comptroller of the Currency (OCC). Our AI-driven solutions can automate the data collection process and enhance the institution's time and readiness to meet regulatory compliance obligations.
For firms in the financial sector, Plat.AI delivers real-time predictive modeling. We provide a server-based solution for any system–regardless of your experience level.
Reduce model risk and increase business value.
Detect, prevent, and manage fraud within an organization in real-time using a single platform. Our machine-learning algorithms can analyze hundreds of thousands of transactions per second.
Get an accurate, flexible, and affordable customer credit risk evaluation. Plat.AI offers financial institutions multiple options for customer risk evaluation:
Machine learning automates time-consuming processes to offer a more streamlined and personalized user experience.
Here are examples of machine learning use cases in the banking and financial industry:
Making Investment Predictions
Financial institutions and investment firms can identify and forecast market changes to make better trading decisions.
Fraud Detection and Prevention
Banks and finance companies that leverage machine learning algorithms can detect and prevent fraudulent financial transactions by analyzing millions of data points.
Risk Management and Customer Retention
Banks and financial institutions can lower their risk with the help of machine learning. It can analyze high volumes of data and make informed decisions based on clients' past behavior. For example, the algorithm can predict customers at risk of defaulting on their loans to help financial institutions adjust terms for each customer accordingly and retain them.
Automated Submission Intake:
Automation complemented by artificial intelligence and natural language processing can extract data from structured and unstructured sources to help underwriters collaborate efficiently and reduce risk decisions.
Process Automation for Better Customer Service
Machine learning-powered technologies allow finance companies to enable process automation and replace manual work. For instance, they can improve customer experience and onboarding through chatbots.
As a result, automation decreases human error, boosts quality, and increases delivery speed. This enables financial institutions to improve their customer experience and onboarding. Moreover, machine learning algorithms can access data, interpret behaviors, and recognize patterns that predict customer churn.
Plat offers a platform that enables you to predict outcomes and trends and identify fraud.