AI

Over the past decades, banks have been improving their ways of interacting with customers. They have adapted modern technology to increase the speed of certain processes in their work. With the digital age bringing up new opportunities like Artificial Intelligence, financial and banking systems are continuously developing. In the last few years, Artificial Intelligence (AI) has achieved far-fetched acknowledgment across businesses with organizations recognizing its value. By 2023, banks are predicted to save $447 billion by applying AI apps. 

How AI Holds the Future of Next-Generation Banking and Finance 

Process Control and Optimization

One of the most significant driving forces of AI in financial organizations is process automation. AI systems can perform complex automation processes. This technology allows scanning of documents, which not only intensifies accuracy but also lowers down the time taken to check regulations and rules in trade finance.

Process optimization is applied in a broad range of departments which include sales, call-centers, accounting, and training and development. Most of these processes are automated, which increases the institution's overall productivity.

Fraud Detection and Prevention

In the financial sector, fraud has always been a constant problem. But with the evolution and development of AI, Financial sectors are deploying predictive analytics into their fraud detection process. Machine Learning and Robotic Process Automation technologies are gaining importance in the fintech industry for identifying patterns that inform fraud-related cases. 

Customer Support

Bots are one of the most famous tools of AI. The fintech industry implemented potent chatbots for the purpose of carrying out interactions with customers. Due to Covid-19 and lockdowns, financial institutions are taking advantage of AI chatbots to solve customer issues remotely.

Decision making

With the advancement of AI, customers can now make use of fintech applications that have data visualization tools. These tools can crack complex data into a simple form that can be understood even by ordinary citizens. This improves their financial decision-making. Without focusing on the recommendations given by human experts, managers are basing their decisions on machine-generated information. These machines can analyze data and provide the most feasible and practical recommendations.

Customer Risk Profiling

For banks and insurance companies and all other fintech companies, categorizing customers based on their risk score is very complex and critical. Using AI technologies like Artificial Neural Network (ANN), the classification models can be trained with historical or pre-labeled data to rate the client profile from low to high.

Monitoring Customer Behaviour

Banks face an increased number of data breaches and cyber threats. Therefore banks can use advanced technologies that will help in determining and identifying potential rancorous activity and thus will be able to ensure a safe and secured online banking experience. Banks can identify abnormalities in the behavior of customers more quickly and effectively by continuously monitoring the behavior of the user. Banks by using a monitoring approach can establish customer behavior patterns and detect whether a transaction is typical for a user or raises suspicion.

Anti-money-laundering screening

As criminal methodologies are growing more advanced, the fight against money laundering is becoming a huge challenge for all the financial and banking sectors around the world. Therefore it becomes necessary to put in AML (Anti-Money Laundering) measures. AI performs AML tasks faster than a human employee and also, through machine learning it possesses the capability to modify new threats and detect new money laundering methods. It ensures that financial firms are able to adjust quickly to different regulatory environments.

When transaction data of a customer is incorporated into an AML program, AI and machine learning models analyze the behavior to make predictions and perceptions about that customer in the future.

Effective Prediction of Cash Flows

For banking and other financial institutions, predicting cash flow is an important and most complex part of decision-making. With the use of AI, cash flow prediction can become effortless for users. AI can learn the pattern of the cash flow and can provide an accurate estimation that is specific to the institution. 

Use of OCR Techniques to Automate Processes

The banking sector can implement optical character recognition (OCR) engines to recognize written letters and characters and digitally regenerate them for future use. This enables the bank to digitize documents and automate invoices which lowers the chance of human errors. OCR can also be used to evaluate paper applications and recognize and alert a user about new arrangements in texts.