Financial institutions are increasingly looking for ways to tap into vast amounts of data collected across the globe to inform critical decisions. Due to the immense amount of data produced and processed within the financial sector, this area has become one of the most likely beneficiaries of applied data science and machine learning. Historically, understanding the complexity of interrelated financial systems and data has been an arduous task. The role of data science and AI in financial transactions has profound implications for the future of the financial and banking industries.
Key trends in data science and AI
Data science and AI, especially machine learning (ML) capabilities like supervised and unsupervised learning, enable organizations to leverage insights beyond traditional structured data by analyzing large volumes of data from various sources and identifying patterns from the data, including within text, audio, and video. These technologies empower the finance sector by swiftly identifying new opportunities, predicting outcomes, increasing efficiencies, providing greater security, and generally facilitating better-informed decision-making.
In particular, the financial technology (Fintech) sector is establishing more secure and reliable transactions leveraging these key technologies. Let us explore a few other critical trends advanced by such technological innovation.
Open APIs enable third-party developers to build applications and services around the financial institution. A data scientist applies data models and algorithms to create actionable solutions. AI and machine learning improve insights and conclusions drawn from the data promoting data-driven decision-making and providing critical services such as appraising customer credit histories more accurately to avoid or reduce defaults.
Mobile banking expands the portfolio of services offered by financial institutions improving customer self-service options and speed of service. The latest trend in mobile banking is the emergence of “neo banks,” which are establishments that operate only online. Neobanks provide consumers with more convenience, lower costs, and better efficiency than traditional banks. AI and data science in mobile banking apps track financial transactions and analyze user data helping banks anticipate the risks associated with issuing loans, such as customer insolvency or the threat of fraud.
Microservice architecture is a new approach to address the complexity of information systems. Complex components are broken into smaller, independent units. Each component may be modified independently to provide new features and services. Using AI and data science within finance and banking microservice architectures helps minimize risk and improve software functionality.
Decision intelligence in finance with data science and AI use cases
Financial firms are constantly looking for ways to improve their operations by obtaining the right data. Doing so can help them create better models and strengthen their competitive advantage. Let’s go through some of the top use cases of data science and AI in banking, finance, and insurance.
Risk management, assessment, and analytics
Finance and banking applications related to data science and AI often involve risk modeling, including analyzing and predicting various factors associated with a financial institution’s risk. Risk analytics mitigates cash flow uncertainty and forecasts future economic stability.
AI in finance can help organizations make better decisions by analyzing and reporting on real-time data, including providing detailed forecasts and predictions. The technology helps in safeguarding against fraud loss, identifying theft, and facilitating cash flow analysis.
Artificial Intelligence can help provide an institution with a more complete and accurate risk view of a potential borrower. It can account for various factors such as a person’s financial history and current financial status with more objectivity than humans. AI-based credit scoring systems are sophisticated and can take into account complex analytical rules including determining loan applicants who are not qualified to receive a loan. Machine learning algorithms are used in digital banking and loan-issuing apps to analyze and provide personalized loan options.
Real-time analytics in finance applications help companies make better decisions by monitoring and reacting to changes in their business systems. This facilitates better customer service and strengthens customer loyalty. With real-time data, financial institutions shorten their reaction time to fraudulent situations, adjust their marketing campaigns or pricing schemes, and monitor customer behavior positioning organizations for optimal responsiveness.
One of the first steps in consumer analytics is to determine if existing channels are working properly followed by modified strategies to attract more customers where appropriate. Data science-enabled consumer analytics provides enhanced insights on customer behavior empowering organizations to quickly shift strategies.
AI in investment management uses alternative data collected from various sources such as corporate reports, filings, transcripts, and media coverage to inform investment decisions and improve hedging strategies. AI can create intelligent outreach programs for customers based on their past behavior and preferences.
Over the last few years, the rise of data-driven investing has been significant. Data-driven trading systems monitor and analyze all available data (both structured and unstructured) in real-time, significantly reducing the time to process transactions. The accuracy of data-driven predictions is significantly higher than those derived from traditional methods. AI helps investors set strong long-term portfolio goals. It can also compile recommendations for the best-performing assets.
Modern industry leaders are looking to robotic process automation to improve their processes and lower their operational costs. With intelligent character recognition, it is possible to perform mundane tasks that historically required a lot of time and effort to complete. AI-enabled software can perform tasks such as data validation, document review, and data extraction. By leveraging robotic process automation, financial institutions materially reduce the human effort required and error rates associated with manual processes.
Data science and AI are rapidly reshaping the way we interact and transact in the financial industry. With the rise of blockchain technology, the industry looks forward to increased account security and lower transaction fees that will benefit the consumer. We can also expect to see more sophisticated self-help and virtual reality systems that improve the customer care experience. Finally, with the new level of transparency created by having more complete, accurate, and timely information concerning each client and their behavior and preferences, financial organizations are expected to deliver more personalized and rewarding services to their customer base, ultimately resulting in improved customer loyalty and competitive advantage.
If you are interested in applying data science and AI to their full potential in the financial industry, email us at email@example.com. Intellect Data, Inc. is a software solutions company incorporating data science and artificial intelligence into modern digital products with Intellect develops and implements software, software components, and software as a service (SaaS) for enterprise, desktop, web, mobile, cloud, IoT, wearables, and AR/VR environments. Locate us on the web at www.intellectdata.com.