Cloud - A Great Refactor for the Financial Services Industry

The banking, financial services and insurance (BFSI) sectors are customer-service driven, document-reliant, and compliance-focused. You know the ongoing challenges. Time-consuming, repetitive data entry tasks across multiple platforms can lead to human error, processing delays, and lost opportunities to personalize marketing and cross-sell products. 

Digital transformation fueled by cloud-based technology is changing the game. Artificial intelligence (AI), natural language processing (NLP), machine learning, optical character recognition (OCR), and intelligent automation are reshaping the future of the financial services industry. Here’s how.

Advantages of Digital Transformation in BFSI

A study by Allied Market Research determined the global digital transformation in BFSI market was valued at $52.44 billion in 2019 and is projected to reach $164.08 billion by 2027. Among the factors driving the transformation have been the widespread use of mobile devices, developments in the Internet of Things (IoT), and cloud technology. 

Intelligent automation including AI, NLP, machine learning and OCR backed by cloud technology can:

  • identify new revenue streams through technology
  • attract (and retain) customers through seamless omnichannel experiences
  • improve decision-making through powerful data analytics
  • mitigate risks through fraud detection and regulatory compliance solutions.

Increased Data Handling Capacity in the Cloud

One of the challenges BFSI encounters is the documentation required in day-to-day financial operations. Much of the required information is on paper, in emails or faxes, or on photocopies or even carbon copies that deteriorate over time. In addition, documentation takes a great deal of storage, is not easily searchable, and can lead to delays, errors, and missed opportunities for cross-selling and personalized customer experiences.

Enter intelligent automation. OCR can digitize data from a variety of sources, including faxes, paper, email and notes, making it accessible and searchable. Machine learning and artificial intelligence can “learn” a financial institution’s systems, identifying and flagging areas of weakness or areas of concern. Documentation stored in the cloud is quickly retrievable, yet takes a fraction of physical storage space.

AI and machine learning can scan, analyze, sort, distribute and file documentation, it can flag discrepancies or missing information, send notifications, follow up for information or escalate, and perform the repetitious data entry, which frees the employees to do higher-value work, such as customer retention or investigating more complex issues or problems. 

AI can scan for customer profiles across omnichannel quickly, and flag potential duplicates or fraudulent accounts. OCR and machine learning can detect anomalies in photo identification and flag for investigation in real-time and can research multiple accounts simultaneously. This level of compliance can provide additional security and protection.

Augmented Customer Experience & Support

Robotic process automation (RPA) can employ intelligent automation and natural language processing to provide an enhanced customer experience. For example, Odigo is a world-leading Contact Center as Service (CCaaS) provider that handles 3 billion customer interactions per year. They have partnered with Global Logics to expand their product’s capabilities. 

One advantage of CCaaS is the ability for companies to only purchase the technology they require, to handle customer service inquiries, chat, email and social media, and other messaging using intelligent chatbots and natural language processing. AI with NLP can escalate to an employee at any point during the interaction, and machine learning means the bots “learn” through interactions, providing more complete and robust information to inquiries based on previous interactions. 

AI can input a customer profile, search for other customer accounts across multiple systems, request a welcome letter or package, confirm identification based on compliance protocols, complete Know-Your-Client (KYC) information, and begin to search for personalized recommendations based on information. 

AI and NLP can provide customer service in the customer’s language of choice, in multiple time zones simultaneously, and can scale quickly to meet increased demand or need. AI can operate 24/7/365, providing an enhanced customer service experience with access to financial services on the customer’s schedule, rather than during traditional banking hours. 

Security & Blockchain Applications in BFSI

Cloud technology provides enhanced business continuity, mitigation of risk and cybersecurity measures. More transactions are being conducted digitally using the IoT – for example, insurance packages can now be customized using a vehicle’s telemetry data. As more of these transactions and processing happen at the edge, the need for more secure hardware and data transmission increases. 

Security access protocols such as multifactor authentication, robust identity access management protocols, continuous monitoring, and encryption can allow for secure transmission between data warehouse/analytics in the cloud and processing at the edge. AI can retrieve information from cloud technology in a fraction of the time it takes a human employee to cross-reference and search information, providing enhanced fraud detection and cybersecurity measures.

Security is only as strong as its weakest component, so it is essential for BFSI to invest in secure hardware and employ multiple encryption and security protocols. Cybersecurity in BFSI is becoming more challenging as cyber-attacks become more sophisticated. One of the ways that the financial sector can protect cloud transactions is to combine AI with blockchain applications. 

Blockchain provides a transparent real-time chronology of transactions using a decentralized public ledger. As each transaction creates a block, every person in the network receives a copy of the ledger. This makes alterations difficult and provides a complete audit trail of each transaction. 

Money transfers, direct payments, transaction tracking, and fraud reduction can be completed quickly using blockchain, as the transaction can be monitored by all parties every step of the way, and blockchain encryption provides an extra layer of security. Blockchain can reduce costs and provide enhanced transparency, an enhanced audit trail, and accountability.

Algorithmic Trading

Machine learning and AI can monitor and track trade volumes, analyze historical trade data, and then use the information to formulate recommendations for future investment strategies. In addition, AI can automatically execute a trade based on preset buy/sell/hold instructions which will be triggered when criteria such as time, price, volume or call and put option instructions. 

As trading volumes increase and client expectations become more complex, the pressure on trading desks to improve execution performance is steadily increasing. Machine learning enables algorithms to “learn” how to make different decisions and consider myriad data points to make smarter trades. Core trading algorithms will become increasingly intelligent and complex, evolving into a sort of contextual playbook versus a strict set of rules.

Final Thoughts

Financial services firms are now using machine learning to predict cash flow events, fine-tune credit scores, and detect fraud, among other important functions. This refactoring of the financial services industry, being driven by advancements in technology and rapidly evolving customer expectations, will propel businesses that are positioned to capitalize on the opportunities to the next level.

With 15+ years in BFSI, including 1200 dedicated engineers and expertise in regulatory compliance and control, Global Logic is helping its partners reshape their businesses – and the industry as a whole. How can we help you embrace these digital trends and transform your business? Get in touch and let’s find out.

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Author

Raja Renganathan

Senior Vice President, Cloud Engineering

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