Financial Services
AI and RPA: The dynamic duo transforming financial services
Technological disruption has impacted all sectors including finance. It has ushered in a new era of digital transactions and transformed the financial sector. The key technologies that have overhauled finance are RPA (Robotic Process Automation) and AI (Artificial Intelligence). These technologies have not only boosted operational efficiency in finance but also enhanced customer experience.
RPA in finance
RPA strategically deploys bots to execute rule-based tasks, enhancing process accuracy and speed. This empowers human talent to transcend monotonous routines, focusing instead on endeavours demanding cognitive ingenuity.
RPA in finance automates manual tasks like accounts reconciliation, financial planning and forecasting, compliance, payment processing, and more. It automates manual tasks, mitigates errors, enhances operational efficiency, improves accuracy, minimises costs, and helps meet compliance mandates. It is because of these benefits that 80% of finance executives have either adopted or intend to adopt RPA.
The limitation of RPA is that it can work only with structured data. It fails to function when data is unstructured. However, businesses require analysis of both structured and unstructured data to gain insights. This is where AI has a role to play! AI makes automation intelligent. AI-based bots can collect and analyse unstructured data as well. RPA lays the foundation on which intelligent automation can be built.
AI in finance
Artificial Intelligence and its subsets ML (Machine Learning), NLP (Natural Language Processing), CV (Computer Vision), Generative AI, etc., have made an emphatic presence in the financial sector and have revolutionised how traditional finance functions. These technologies help to gain a better understanding of markets and customers based on which financial service organisations can engage with their customers in a manner that mimics human intelligence and interactions.
AI is deployed in fraud detection, credit decisions, customer service, risk management, compliance, portfolio management, and more. It is also leveraged in asset management, trading, risk analysis, etc.
How RPA and AI complement each other in finance?
- When RPA and AI work together they can enhance each other’s capabilities and provide robust solutions.
- RPA handles repetitive manual tasks, while AI introduces intelligence in decision-making. For instance, RPA automates document processing and AI analyses those documents to detect trends or make forecasts based on historical data.
- While RPA bots can extract structured data from documents such as invoices, AI can process unstructured data like emails or PDFs to facilitate better decision-making or generate insights.
- RPA automates rule-based workflows and AI manages cognitive tasks that require more advanced thinking. The combination not only speeds up workflows and increases accuracy but also helps to understand customer behaviour and make real-time decisions based on data analysis.
Benefits of AI and RPA in finance:
Cost reduction: Automation helps financial services organisations minimise operational costs by reducing the need for human intervention in mundane tasks. Data reveals that by 2030, AI in finance will generate over USD 1 trillion in global savings and revenue.
Accuracy: RPA and AI work in tandem to minimise human errors, especially in critical tasks like compliance reporting, secure payment processing solutions, risk management, etc.
Improves decision-making: AI algorithms provide deeper insights by analysing vast volumes of data and uncovering trends helping financial institutions make informed decisions.
Speeds up processes: Automation speeds up processes facilitating quicker turnaround times in areas like customer service, credit approvals, transaction processing, and more.
Scalability: RPA and AI solutions can be scaled up to handle increased volumes of work without the proportional need for human resources.
Enhances customer experience: RPA and AI increase the speed and accuracy of processes which reduces delays and provides a better customer experience. AI analyses customer data and provides insights based on which financial services organisations can offer customised products and services to customers. AI-based chatbots respond to customers in a human-like manner and offer 24/7 support to resolve their queries leading to satisfied customers.
RPA and AI use cases in finance
Personalised banking: Bots analyse user data for spending insights and offer personalised financial advice through mobile apps.
Financial forecasting: ML algorithms can forecast financial trends that pave the way for strategic planning.
Loan processing: AI systems automate financial document analysis in loan processing applications speeding up processing time.
Automated underwriting: AI can automate underwriting providing instant risk assessments and providing quotes.
Fraud detection: Automation tools identify anomalies in transaction data to detect spurious activities.
In conclusion
RPA and AI are transforming the financial sector by streamlining processes, enhancing decision-making, improving customer service, ensuring compliance and mitigating risks. The ongoing advancement of technology will cultivate increased opportunities for financial institutions to enhance their service delivery to clients.
How can Infosys BPM help?
Infosys BPM’s financial services can help organisations to transform their operating models, standardise processes, boost efficiency and minimise costs. We leverage cutting-edge technologies to provide customised solutions that help enhance operational efficiency in finance.
