The demand value for AI in the BFSI industry is expected to grow by 35% CAGR in 2028. To top it off, a report by the PwC-FICCI suggests that 83% of the CIOs in a survey favored AI implementation for better financial service management and customer experiences.
While all these studies indicate a positive inclination towards AI, its capabilities and potential have to be further explored so that banks can onboard these transformative technologies and solutions without risk. This blog discusses 16 best use cases for cloud-native AI in BFSI along with highlighting how cloud can help in capturing the full potential of this indispensable technology. Keep Reading!
Unraveling the 16 Best Use Cases of AI in BFSI
AI-backed Cognitive Document Processing
BFSIs deal with humungous volumes of documents such as KYC documents, financial reports, and contracts. What's even worse is that most of these documents are processed manually which substantially consumes time and effort. AI-driven cognitive document processing helps to automate the organization, input, and evaluation of such documents in a quick, secure, and cost-effective manner.
Hyper personalized Banking Experiences
AI empowers BFSI to develop next-generation business models that focus on delivering personalized customer experiences and promoting financial inclusivity. By utilizing multilingual support and sentiment analysis, AI in banking can create relatable, customized, and timely insights for end customers, enhancing experiences leaps and bounds. Not to mention, AI-driven big data analytics can help in analyzing real-time customer behavior and feedback, enabling recommendations for building new financial products and services.
Customer Support Management
Considering that 60-70% of query handling is processed via phone calls, the customer support team in the BFSI industry generally spends long hours over these calls often without any proper resolution. On top of this, the handle times were stretched to 15 minutes with organizations achieving less than 40% First Call Resolution (FCR) rates. However, with the implementation of AI in banking, 70% of email, call, chat, and mail inquiries get drastically reduced, thus increasing the scope for agents to address personalized queries. This is because these AI-based chatbots are integrated with automated coaching and an upgraded knowledge database to resolve basic queries that don't require human intervention. Agents can add improvements to their customer calls through data backed by AI-driven real-time analytics.
Along with this, voice AI helps in developing advanced algorithms that support code-switching, complex language models, noise filtering, and actionable data.
What's more, issues of mis-selling policy and coverage are very common in the financial industries. his is where automated QA comes in. This AI-backed solution helps minimize manual auditing and random sampling of calls, ensuring that every call is assessed for any compliance issues. At the same time, automated QA measures the performance of the agents and suggests improvement areas.
With AI, organizations can automate business rules to filter the steps required for accepting claims. Apart from automating the validation process, AI solutions are used for:
- Cross-checking policy details
- Flagging off loss claims
- Designating claims adjusters based on the behavior in loss claims management
- Allocating surveyor depending on customer desirability and claims handling proficiency
- Analyzing and filtering off fraudulent claims
- Reducing claim mismanagement and false actions
What's more, insurance agents can predict which claims will result in litigation propensity by enabling AI technologies. These AI tools range from pattern analysis, exploratory analysis, to forest algorithm allowing insurers to focus on risk assessment and prioritization of claims that can lead to potential lawsuits.
There's no doubt that monitoring real-time transactions for fraud detection is a big-time hassle. Utilizing AI can help to analyze fraudulent patterns in transactions. AI algorithms then separate the fraudulent transactions from the authentic ones. These algorithms consider hundreds of variables pertaining to a specific transaction like location, amount, mode of payment, etc. Based on this detection, the companies either alert the concerned parties or block the particular transaction if it is a serious fraud
By implementing AI-based fraud detection tools, organizations can handle huge volumes of data to rule out any fraudulent patterns that are not easily detected by humans and minimize false positives. On top of that, AI-generated fraud detection technologies are cost-effective when compared to traditional methods that generally rely on manpower and human expertise.
One of the biggest challenges that BFSI professionals face is regulatory reporting. This requires them to review the regulations, assess them, prepare a report on how they can apply a specific regulation to the business process, and convert the text into code for data retrieval. Implementing AI in banking and financial services can smoothly parse unstructured regulatory data to describe reporting needs, assess it based on past rules, and develop codes. This code deploys an automated process that helps to access various company resources to develop the report. Due to this regulatory intelligence, organizations can enable quick submissions for new product or service approvals.
Since the BFSI uses traditional transaction monitoring tools, cases of false positives reach up to a whopping 90%. With AI-backed monitoring systems, organizations can reduce false compliance alerts and review costs. As low-risk issues can be resolved by AI, the high-risk issues can be directly sent to the compliance officers. Since compliance professionals can deal with only high-risk issues, they can also focus on the core strategic part of the business. With emerging AI trends, AI can also be utilized to modernize legacy transaction monitoring systems as well.
Customer Churn Analysis
To improve the lifetime value (LTV) of customers, it's really important to analyze customer behavior and trends. Deploying AI-powered predictive analytics can help BFSI companies acquire relevant insights into new customer markets, enhancing the process of customer acquisition. Not only this, but predictive analytics also offers insights into which customers will switch to other options and their reasons for doing so. This helps organizations build strategies to minimize customer abandonment.
For retaining customer loyalty, AI-driven analytics in banking and insurance offers information into existing customer habits and spending behavior which helps marketing professionals to build loyalty programs. Monitoring customer satisfaction is also equally important given that the cost to gain new customers will always be greater than the cost to retain an existing customer pool.
Without any human intervention, AI helps forecast market predictions, risks, and trends that are accurate and error-free. In addition, AI combines non-traditional data (like stock availability and weather data) with financial data to assess metrics' relations and gain clear insights into expected sales and revenue. More and more BFSI companies are switching to developing agile, accurate models that help them leverage data for the latest market trends, analyze risks, allocate manpower, and enable accurate information for better financial planning.
By implementing AI tools, marketing teams can scrape through the internet to find which hashtags and buzzwords are trending, news about any competitors, or any new services launched. Even the latest happenings and trends in the BFSI sector are collected and sent across for real-time analysis.
Algorithmic Trading has been dominating the financial markets for some time. AI solutions monitor trade results in real-time to identify patterns that prompt stock prices to go up and down. Apart from this, using AI in algorithmic trading reaps the following benefits such as:
- Enhanced accuracy with zero human errors
- Enables trades to be implemented at the right prices
- Automates market analysis
Generally, traditional risk management models assess credit scores to assign risk levels. However, with the advent of AI, organizations can analyze huge datasets of personal information to minimize risk levels. This is because the insights provided by AI tools help companies leverage actionable intelligence to make informed decisions. For instance, in the case of loan applications, AI can analyze multiple data sources for such customers and determine risk scores based on the information gathered. At the same time, AI solutions can gauge which customers will turn to loan defaulters and help companies rethink the terms for each customer.
Here are 2 ways in which AI-powered wealth management solutions can help financial companies smoothly manage their client's money.
Quick Client On-boarding
Using AI can automate and optimize manual onboarding processes like document verification, risk profiling, anti-money laundering inspections, and legal documentation. This automated process increases productivity and customer satisfaction as employees can gain quick and easy access to such information and build a more meaningful, value-added relationship with the clients.
Typically, wealth managers were tasked with portfolio optimization which involved evaluating vast data sources within a stringent deadline. But AI is about to change that. Implementing AI solutions in banking and financial services can optimize portfolios by monitoring contextual indicators and enabling a data-centric approach.
By adjusting portfolios to offer more timely and relevant services, clients don't have to fret over any unforeseen changes or dubious money managers restricting their returns. In addition, deploying AI in portfolio management offers insights and recommendations for building strategies around portfolio rebalancing. To rebalance investor portfolios, companies need to consider various economic factors and demographic data. So, based on these insights, managers can add improvements to their investment strategies and even customize their products and services.
Did you know that in the BFSI sector, cybercrimes can lead to a potential damage of $10.5 trillion by 2025? Shocking as it sounds, the truth is that traditional cybersecurity solutions are not cutting the mark anymore. It's important to make the switch to AI-augmented cybersecurity tools. With cyberthreats evolving at a warp speed, here are some ways in which AI can secure BFSI businesses
Behavioral Analysis and Vulnerability Management
AI can help in assessing patterns in user behavior, servers, endpoint activity and digital signatures to discover any suspicious activity. This can include changes in click speeds, unusual background activities and sudden traffic spikes. If there is any potential risk, the AI tools send automated alerts to speed up the remediation process. Apart from this, AI tools also examine both structured and unstructured data sources like social media, web, and in-house reports to identify any potential anomaly.
Boosting Resolution Times
When combined with traditional security solutions, AI can augment threat detection rates by 95%. This is because AI can automate threat identification processes by drawing parallels between malware files and IP addresses. In doing so, the security teams can identify security anomalies 60 times faster.
Implementing Augmented Analytics
The powerful combination of AI and augmented analytics can scrape through hundreds of compliance and regulation reports to pinpoint important suggestions and recommendations. Likewise, by automating security processes like detection, monitoring, approval, and decline decisions, security teams can focus on more strategic tasks that need human expertise.
While generative AI is making waves in other industries, the BFSI sector is not far behind in leveraging this powerful tool. With the strategic adoption of generative AI, tasks like reasoning over data, summarization of reports and writing assistance will be automated and more streamlined. When integrated with chatbots and virtual assistants, companies can offer more customized services to their clients. Moreover, generative AI boosts the productivity of the employees in the banking sector as it automates processes like code suggestions, content generation, and knowledge retrieval. This helps the team to invest their efforts into more strategic business activities. Here are other use cases of generative AI in the BFSI sector:
Generative AI in Banking
With generative AI, companies can explore different avenues for augmenting revenues with data-driven insights and superior customer experiences. For example, one of the largest banks in America employs generative AI to analyze huge volumes of customer data and financial transactions for detecting credit exposure. Based on this, analysts can make accurate lending decisions thereby minimizing the risks of loan defaulters.
Generative AI in other financial services
BFSI can deploy generative AI to secure and customize their business processes. By leveraging new models and algorithms, generative AI replicates scenarios for finance teams to design creative and innovative banking solutions. Other than that, this technology can be used to customize financial guidance and deliver next-gen conversational banking services.
Smart Wallets and Robo Advisors
Robo advisors have been built to make the lives of wealth managers easy. How? Basically, robo advisors are online platforms that employ AI algorithms to develop portfolios based on parameters like risk tolerance, credit score, income, etc. They offer financial insights and handle investment portfolios with zero human intervention. On top of that, robo advisors also help in rebalancing portfolios, executing tax loss harvesting methods, and reinvesting dividends for the customers. Meanwhile, the concept of smart wallets is gaining traction in the BFSI industry. By integrating AI intelligence into the wallets, BFSI companies can offer quick smart services to customers like utility bill payments, video/virtual chat booking of taxis, buses, and movie events.
ESG Data Standardization
Another concern area for BFSI is Environmental, Social, and Governance data standardization. Due to the lack of standardized ESG regulations, BFSI companies utilize their own standards and templates to prepare reports for their ESG activities. AI comes as a silver lining to this conundrum. Here are some ways in which AI techniques can help in the seamless standardization of the ESG data.
- Natural Language Processing: By enabling NLP libraries like NLTK or spaCy, AI can deploy tools for named entity recognition and part-of-speech tagging. These techniques help in extracting ESG metrics from texts, reports, and documents.
- Clustering Analysis: By leveraging learning algorithms like K-means clustering, this AI model detects clusters or groups similar ESG metrics in a dataset. Managers can analyze the relations between various ESG metrics, boosting taxonomy development.
- Topic Modeling: With techniques like Latent Dirichlet Allocation (LDA), the AI model detects latent topics within ESG datasets. Once the common themes and keywords are discovered, the AI solution helps in developing and standardizing ESG taxonomies.
- Semantic Similarity: Deploying techniques like Universal Sentence Encoders and Word Embeddings, AI solutions can evaluate the semantic similarities between the reporting framework obligations and ESG datasets. Based on this data, managers can enable accurate mapping between reporting fields and ESG data.
- Statistical Analysis: By implementing statistical analysis, AI can scope out anomalies or discrepancies in the ESG data. This can comprise techniques like regression analysis, data distribution analysis, or hypothesis testing to ensure the consistency and accuracy of the ESG data.
Cloud and AI: A Dynamic Combo
While we discussed the invariable benefits that AI can bring to BFSI companies, it is important to note that developing an AI algorithm is an extremely challenging and expensive affair. Let alone deploying and managing it. Though there are large companies that would be willing to shell out additional costs in designing their own, in-house AI algorithms, the same can't be said for small or mid-sized companies. So, how can BFSI leverage AI to its full potential? Enter cloud. With in-built cloud-native AI tools, one need not worry about building infrastructure for running these tools. This is because these tools have already been tested on big datasets and can easily be custom-trained and plugged in. Furthermore, companies save themselves from bearing the costs of upgrading the AI algorithms and tools.
Adding to this, Cloud acts as a lever of transformation by scaling cloud-native AI technologies to meet the dynamic demands of the business environment. Listed below are the cloud-enabled AI innovations that are predicted to create breakthroughs in the BFSI landscape:
Next-gen Business Models: Integrating AI into Pay-as-you-go insurance models automates real-time claims processing and payment, and embedded finance. It helps in creating super apps that enhance better discovery, personalized investment journeys and investment architecture.
Superior Customer Experiences: Using AI to build cloud-based recommendation engines can customize customer fulfilment and enrichment journey. Meanwhile developing a cloud-native UPI processor can help in automating and streamlining high-traffic management and support huge volumes of merchant collections and UPI transactions.
Setting up Centers of Excellence: With the establishment of Centres of Excellence, cloud experts can help in deploying AI technologies to help BFSI companies produce innovative financial products for customers, enhance customer satisfaction, curtail costs while ensuring strict adherence to new compliance requirements.
Accelerating BFSI Transformations on Cloud, the Cloud4C Way!
With new AI trends reimagining the BFSI landscape, organizations need a combination of robust expertise and technical proficiency to help them navigate through the challenges of AI implementation.
Cloud4C, one of the leading managed services providers, helps to establish an advanced AI infrastructure across banking environments. By integrating AI and deep learning capabilities with data analytics, we help banks reinvent their business operations, service delivery, decision-making and customer management. What's more this solution suite comes with tools like AI-driven data modelling, recommendation engines, video analytics, image, speech/text video analytics and more.
If that's not all, our AI-powered platform called SHOP integrates powerful solutions for delivering managed cloud services to banks and financial institutions. necessary to deliver managed cloud services to enterprises. As this platform brings different applications into one single system, banks can gain a 360-degree view of their IT infrastructures. As a result, banks can resolve outages, assess risks, prevent risks, optimize risk responses, and modernize their banking operations while achieving an overall efficiency of 50%.
Are you curious to know how our cloud-native AI tools can transform your BFSI company? Visit our website and get in touch with our representative today!