The financial services industry has always been at the forefront of adopting cutting-edge technologies to streamline operations, enhance customer experiences, and improve overall efficiency. In recent years, intelligent automation has emerged as a game-changer for financial institutions, which is transforming the industry in every possible way.
Some of the key benefits that we have seen are evident in the customer experience.
Chatbots and virtual assistants have enabled round-the-clock support to customers through answering simple queries, and even assisting with basic financial transactions. This has cut down on response times while ensuring consistent and accurate information to resolve issues. Juniper Research has found that the operational cost savings from using chatbots in banking will reach $7.3 billion globally by 2023. Additionally, intelligent automation enables personalized recommendations and services based on customer data and behavior analysis. This has inevitably led to business growth in many ways.
Now the industry is slowly adapting robotic process automation (RPA), cognitive automation, and artificial intelligence (AI), which is transforming the ways these organisations operate on a daily basis. But, what remains crucial to understand is how these organisations will strategize the implementation of these newer technologies to benefit the most.
In a nutshell, let’s understand how RPA can change the dynamic and data-intensive world of finance. Going forward, we will see that it plays a pivotal role in automating repetitive, rule-based tasks such as data entry, account reconciliation, and transaction processing. This will not only minimize human error but also enhances efficiency across organisations as it automates several back-office operations. Moreover, RPA enables 24/7 operations, ensuring that critical tasks are completed without interruption, leading to improved customer service and compliance. The scalability of RPA allows financial organizations to adapt swiftly to changing market conditions and regulations, maintaining competitiveness and regulatory compliance. Overall, RPA empowers financial institutions to operate more efficiently, reduce operational risks, and provide better service to their clients in an increasingly complex and fast-paced industry. Additionally, RPI and Artificial Intelligence can see faster results with the deployment of Cognitive RPA. This helps in leveraging AI capabilities such as machine learning, natural language processing and data analytics to interpret financial data, detect data anomalies, making the process more efficient. This is just the tip of the iceberg, as it is estimated that RPA in the banking sector is expected to reach $1.12 billion by 2025.
Another significant process of this industry can be accelerated through machine learning. These models can analyze market data, credit scores, and customer behavior to predict potential risks and opportunities accurately. Additionally, automated systems can quickly assess loan applications, evaluating creditworthiness and generating risk profiles. This not only speeds up the process but also reduces the likelihood of bad loans, ultimately improving the institution’s bottom line.
Further, a proactive approach helps financial institutions detect and prevent fraudulent activities before they cause significant losses. Furthermore, automated systems can enforce security protocols, such as two-factor authentication and biometric verification, ensuring that customer accounts remain secure. The combination of AI and automation strengthens cybersecurity measures and builds trust among customers.
Automation in the financial service sector can be further accelerated by Semantic Artificial Intelligence solutions, which relies on knowledge graphs and natural language processing (NLP) to understand the overall business context and provide intelligent solutions. This in turn helps to improve the accuracy and speed of the processing. The basic understanding of how different entities relate to each other can offer additional context for the machine learning models to provide better results. Banks can deploy these solutions especially in self- service portals or customer education programs to improve the personalized offerings and/or service.
Data Analytics and Insights Financial institutions deal with enormous volumes of data daily. Intelligent automation can sift through this data to uncover valuable insights and trends. By harnessing the power of AI and machine learning, financial organizations can make data-driven decisions, optimize investment strategies, and identify opportunities for growth. Furthermore, automation facilitates real-time reporting, enabling organizations to stay agile and respond swiftly to market changes. This competitive advantage can be critical in the fast-paced world of finance.
Large Language Models (LLMs), although in its early stages, are seeing tremendous interest. Large Language Models (LLMs) can interpret complex financial information and provide insightful recommendations by reviewing large scale industry and sector specific data. Although it offers strong capabilities, some of the issues like model Bias, Hallucination and data privacy issues remain a hurdle to large scale deployments.
Intelligent automation is disrupting every industry and finance is undoubtedly at the forefront. As technology continues to evolve, financial institutions that embrace intelligent automation will remain competitive and better positioned to meet the evolving needs of their customers and regulators. By harnessing the power of automation and artificial intelligence, financial services organizations can navigate the complex challenges of the modern financial landscape with confidence and efficiency.
The article has been written by Ramesh Tunga, Associate Director, and Head of data science community, Acuity Knowledge Partners