04 Jun How does Artificial Intelligence enable organizations in the financial industry to increase their competitive edge?
The explosion of banking and financial data is driving the adoption of Artificial Intelligence in the finance industry. Artificial Intelligence or AI is already playing a disruptive role in the banking and financial industry sector.
For instance, AI banking is improving the quality of banking products and services. According to McKinsey, AI and banking can deliver up to $1 trillion of business value each year. In its Global AI survey report, McKinsey reports that 60% of financial services companies have embedded at least one AI capability.
The use of AI in the banking and finance domain is also improving customer experience and backend processes. Why do the banking and financial domain need AI technology to improve their market performance? Let’s discuss this over the next sections.
Role of AI technology in Banking and Finance
Traditionally, AI technology is the creation of “intelligent” machines (or AI models) that can self-learn and perform tasks just like human resources. AI models can organize and interpret financial data and provide useful business insights based on this data.
AI in finance is disrupting the “traditional” domain of financial products and services – and providing innovations that lead to new products and operational models.
Next, let us look at the various ways in which AI technology is transforming the banking and financial industry.
AI applications in Banking and Finance
Here are some banking and financial applications where AI has made a significant contribution:
Along with Natural Language Processing or NLP capabilities, AI-powered cognitive chatbots have enhanced 24/7 interactions with customers. AI-based chatbots can optimize the cost of customer service by providing responses to regular customer queries like their account balance or generating an account statement. Among the latest developments, advanced chatbots can handle complex customer queries and direct their complaints to the right customer agent or service department.
2.Detecting and preventing financial fraud
Traditional and rule-based systems for anti-money laundering (AML) activities generated a heavy volume of false positives. The recent increase in financial fraud has prompted banks and financial companies to use AI models to detect any data anomalies or suspicious activity. AI in fraud detection can detect “new” financial transactions, which had previously never been detected until they were complete.
Thus, AI can effectively prevent financial fraud – instead of the traditional models that had a reactive approach toward fraud.
The recent growth of digital and mobile banking means that more customers want to perform their bank transactions using digital channels instead of visiting their nearest bank branch. Between 15-45% of banking customers want to reduce their visits to a physical branch or office, post the pandemic. With the emergence of digital banking, customers now expect personalized banking services that are customized to their financial goals.
To meet the changing customer expectations, an AI-powered bank can personalize its offerings (based on the customer’s previous transactions) and enable a consistent omnichannel experience (across all connected devices).
When combined with data analytics, AI in the finance domain can be used to accurately forecast stock prices and revenues. AI-powered predictive analytics is being deployed to extract useful insights from vast volumes of customer data. Increasingly, predictive analytics models are being used to “flag” any suspicious activity.
The rapid increase in generated customer data has been instrumental in improving the performance of AI-powered predictive analytics models. This has effectively reduced “guesswork” and the need for human intervention in the financial domain.
The role of AI technology has been profound in the area of financial risk management. With access to vast data volumes and processing power, AI models and algorithms can quickly perform a risk analysis, which would previously take too much time for human resources.
By harnessing customer data, AI can be accurately used to determine the creditworthiness of any loan borrower or credit card user. Instead of relying on expert judgment, AI in investment banking can be used to determine the risk profile of any customer. This can reduce the overall credit losses incurred by any bank or financial institution.
According to Ernst & Young, robotic process automation (or RPA) technology is reducing the cost of human-performed (or manual) tasks by 50-70%. Forbes refers to RPA as the “gateway drug to digital transformation.” AI-enabled RPA is enabling financial institutions to reduce their operational costs and improve their productivity.
For instance, the use of intelligent character recognition is automating repetitive and time-consuming banking tasks that would take hours for human employees. Along with reducing the impact of human errors, AI-driven process automation is enabling banking employees to focus on high-end processes that demand advanced cognitive skills.
Artificial Intelligence in Finance – Our perspective
At NuMantra Technologies, we enable our financial domain customers to extract enormous business value from their structured and unstructured data. Our standardized hyperautomation platform provides the ability to build AI and machine learning models to reveal hidden patterns from large volumes of financial data.
These models can be developed using a host of open-source tools and third-party products as well as custom algorithms including:
- Python Scikit Learn
- Microsoft Azure
- Amazon SageMaker
- IBM Watson
- Google Cloud Tesseract and TensorFlow
Along with AI and machine learning, our hyperautomation platform offers Robotic Process Automation (RPA) which enables end-to-end automation of “manual” banking processes. At NuMantra, we believe that organizations can leverage the full capabilities of AI/ ML and RPA by first analyzing and streamlining their processes using process mining. The business insights provided by process mining can be combined with external business data to develop AI/ML models that can predict future business outcomes and lead to better decision-making.
Our banking and financial customers have benefited from over 20 years of industry experience and gained a competitive edge in this industry. If you are looking for the best of customized solutions in AI and RPA, place a request for a product demo today.