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BFSI and intelligent automation
- Featured Insights
- December 1, 2023
How Intelligent Automation Is Transforming The BFSI Industry
Intelligent Process Automation is scarcely reachable today. Being abundantly available to multiple verticals of different industries, it is now being harnessed by every sector. AI (Artificial Intelligence) and RPA (Robotic Process Automation) solutions are known for making a concrete impact on the BFSI side of business. This is achieved by streamlining laborious tasks and maximizing the derived advantages. The necessity for implementing these solutions arises from the massive volume of data this industry must process, bought by the diverse range of services provided by automation technologies.
Technologies that enable Intelligent Process Automation in BFSI Industry
Robotic Process Automation
By Automating mundane tasks and the ones that are governed by the same set of rules, RPA leads to a reduction in manual errors and back-office operational costs.
Artificial Intelligence and Machine Learning
Critical tasks like credit scoring, risk assessment, detection of fraud, and personalized customer services are enhanced by AI and ML. Making fraud detection an automated process, for example, substantial money for the BFSI industry
Natural Language Processing
NLP (Natural Language Processing) enhances efficiency in the BFSI industry by automating data analysis and extracting valuable insights from unstructured data, improving the decision-making process and customer experiences.
Data Analytics and Big Data in BFSI
Analyzing large datasets of financial information to identify trends, patterns, and customer behaviors is crucial for tailoring personalized marketing strategies, assessing risks, and devising effective investment plans.
Blockchain technology
Blockchain offers a secure and transparent avenue for executing financial transactions, handling digital identities, and simplifying operations such as cross-border payments and smart contracts, enabling the BFSI industry towards better automation use.
Internet Of Things
In the BFSI sector, IoT devices find utility in asset tracking, risk management, and customer interaction.
Cloud Computing in BFSI
Cloud computing provides scalable and cost-effective storage solutions for the substantial data volumes produced by BFSI organizations. It fosters convenient data access, promotes collaboration, and facilitates the implementation of a diverse process automation cycle and AI applications, eliminating the need for extensive on-premises infrastructure.
Challenges In The BFSI Industry While Transforming To Intelligent Automation.
Although the BFSI industry enjoys numerous benefits of automation technologies, it still struggles with managing the challenges of transitioning its business to intelligent automation. Here is a list of the challenges they face:
Data Security and Privacy
BFSI deals with extensive customer-sensitive data that requires careful handling during the implementation of automation.
Operational risk and performance: The BFSI pillars
Despite the substantial data volume requiring decision-making and calculations in every process, relying excessively on process automation and AI can potentially lead to erroneous decisions or system failures. Additionally, performance issues may surface because of the data load.
Change management
Embracing changes is invariably challenging, especially when it involves automation. It is crucial to help employees understand how process automation can enhance their overall operational efficiency.
Regulatory Compliance
The BFSI sector operates under strict regulations, making implementing automation while ensuring compliance with ever-evolving regulations a significant challenge.
Cost management
Initiating your automation journey necessitates an initial investment that relies on the tools and technology chosen to automate identified processes. Infrastructure setup is also a vital step in this process.
The BFSI sector has matured sufficiently to confront these challenges by implementing robust and scalable solutions, prioritizing long-term ROI, conducting training programs, and embracing stringent cybersecurity measures.
Use Cases That The BFSI Industry Has Already Implemented.
Virtual assistant for BFSI
Problem Statement
A key challenge for a prominent financial institution was the overwhelming volume of customer queries about account balances, transaction histories, and general banking information. This influx strained the customer service department’s resources, leading to customer dissatisfaction.
Solution
Integrating a virtual assistant powered by natural language processing (NLP) and artificial intelligence (AI) into the institution’s website and mobile application enables customers to interact through text or voice commands. The virtual assistant can offer real-time responses, facilitate basic transactions, and assist users in navigating the institution’s services.
Automated customer onboarding
Problem Statement
An insurance company encountered manual and time-consuming customer onboarding difficulties, resulting in delays in policy issuance, customer dissatisfaction, and heightened operational expenses from extensive paperwork and manual verification procedures.
Solution
An automated customer onboarding system integrating data capture technologies, optical character recognition (OCR), and AI-driven document verification allows customers to upload identification documents and relevant information via the company’s website or mobile application. The system validates the information, conducts background checks, and assesses risk profiles to accelerate onboarding.
Fraud detection and prevention
Problem Statement
The substantial rise in fraudulent transactions has caused financial losses and decreased customer trust. The existing fraud detection system has struggled to keep up with the evolving techniques employed by fraudsters, leading to a considerable number of undetected fraudulent activities.
Solution
Implementing an advanced fraud detection and prevention system relies on machine learning algorithms and predictive analytics. This system analyzed transaction patterns, customer behaviour, and historical data to identify anomalies and potentially fraudulent activities promptly. Additionally, it integrated behavioral biometrics and device fingerprinting to bolster security and ensure the precise detection of suspicious transactions.
Data analytics and prediction
Problem Statement
The organization’s inability to accurately predict market trends, investment opportunities, and customer preferences led to suboptimal investment decisions and a decline in portfolio performance, affecting its market competitiveness. The absence of robust data analytics capabilities further constrained the firm’s capacity to harness market insights and deliver personalized investment strategies for its clients.
Solution
Implementing a comprehensive data analytics and prediction system incorporating historical market data, customer investment profiles, and global economic indicators. Utilizing advanced predictive modeling and machine learning algorithms, the system analyzed market trends, recognized investment opportunities, and forecasted potential risks. Additionally, it offered personalized investment recommendations tailored to client risk tolerance, financial objectives, and prevailing market conditions.
Internet of Things for BFSI
Problem Statement
A challenge is accurately assessing risk factors associated with insured assets, such as vehicles and properties. The lack of real-time data on asset conditions and usage patterns hindered the company’s ability to offer personalized insurance premiums and effectively mitigate potential risks.
Solution
The company obtained real-time data on location, usage, and environmental conditions by integrating IoT sensors into insured assets like vehicles and properties. This data was transmitted to a centralized platform and analyzed using data analytics and AI algorithms to assess risk levels and establish customized insurance premiums. Moreover, the data was utilized to provide proactive risk management services and timely alerts to customers in the event of potential threats or incidents.
Impact And Reason Why BFSI Industries Are Using AI and Automation.
Enhanced operational efficiency
Enhanced application efficiency and transparency increase trust in the services provided by the BFSI industry.
Improved customer experience for BFSI
Increased trust has reduced customer wait times and improved overall customer satisfaction, enabling human resources to concentrate on more intricate and specialized customer concerns. This has resulted in higher retention rates and increased engagement on its digital platforms, thus, a better understanding of customer behavior and requirements.
Risk management
Risk assessment has resulted in multiple things. Nonperforming loans have decreased, creating a healthy loan portfolio. Companies have also secured better compliance, strengthening their market reputation. Enhancing trust, advanced risk assessments, and personalized insurance offerings minimize losses, reinforcing the insurer’s commitment to customer safety and security.
Market Analysis
Organizations analyze extensive data sets through process automation to identify market trends, adjust investment strategies, mitigate risks, and optimize client outcomes. This also makes it easier to detect threats and fraud.
Conclusion
Automation and AI represent a significant revolution in the BFSI sector, and their influence continues to grow. Each BFSI entity is transforming a digital landscape, fortifying its capabilities in AI and automation. Intelligent automation transforms BFSI, ushering in efficiency, security, and customer-centricity for a groundbreaking era in the industry. Leveraging intelligent automation, BFSI innovates for sustainable growth, reshaping the future delivery and perception of financial services.
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