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AI in KYC: What’s in Store for the Digital Insurer and Customer

2 minutes, 4 seconds read

Know Your Customer or KYC is a crucial step towards ensuring that any financial malpractice, money laundering scams, terrorism financing, and other illegal corruption schemes are cut at their source, thus becoming a primary aspect for customer identification. It is also the first step in the client onboarding process. 

KYC procedures include ID card verification, face verification, document verification such as utility bills as proof of address, and biometric verification.

Banking institutions must strictly comply with KYC regulations and anti-money laundering regulations to curb fraudulent activities. KYC compliance responsibility rests with the banks, failing which they might also be heavily penalized. 

International regulations such as The Financial Action Task Force (FATF) are also now implemented in national laws encompassing strong directives namely, AML-4 and 5, and preventive measures such as KYC for accurate client identification.

Artificial Intelligence takes KYC and AML compliance to a new level by combining related technologies that offer the potential to automate workflows and analyze large volumes of varied data. 

The AI Future: 

AI ensures intelligent decision-making and overall monitoring that helps to battle risks and frauds associated with financial institutions. It’s programmed to comb through large mounds of data, process and thereafter verify client profiles, as well as, enhances due diligence in an organization. Machine Learning (ML) clubbed with AI augments the smooth running of operations, particularly in labor-intensive areas. 

With its innovative technological revolution, AI-based technologies are changing industries worldwide through automation and machine learning. Banks and other financial institutions have so far benefitted in terms of Workflow Automation, Link Analysis, Maintaining Compliance, and Regulatory Changes, among other processes. 

Image credit: www.infosysconsultinginsights.com

KYC Automation, Benefits, and How It’s Better Than Manual KYC: 

KYC automation leverages advanced AI and machine learning technologies to ensure that all regulatory standards are met without a high dependency on internal resources. 

Even though end-to-end KYC processing still requires humans to make high-level decisions, a majority of the processes can be taken care of using automation, or Intelligent Process Automation. 

Intelligent Process Automation (IPA) includes Robotic Process Automation (RPA), Intelligent Document Processing (IDP), Intelligent Character Recognition (ICR), and Artificial Intelligence (AI). This collection of technologies combine the entire management, automation, and integration of digital processes. These are also now being used to automate workflows, extract data from documents and reduce the time taken for screening, identification, and verification. 

Why choose automation? 

From cost reduction, more efficiency, minimized risk, and more, KYC automation is more helpful than manual processes used in the past. Here’s a detailed look: 

Cost Reduction: Automated solutions enhance KYC processes and reduce onboarding costs by over 70%. By substantially eliminating data entry errors and any required rectification, hefty non-compliance fines, thereby delaying the onboarding cycle, banks and financial institutions can significantly reduce costs.

Increased Efficiency: A fully automated identity verification process enables customers to create a verified digital identity instantly. Automated solutions also have the added benefit of running round-the-clock with no downtime.

Low risk: Automation helps to minimize the risk of errors caused by any unfortunate data entry mistakes or oversights. The reduced manual intervention also significantly reduces security threats and data breaches, thereby keeping customers’ data safe and banks compliant.

Improved Customer Experience: A great customer experience is the key to a business’ success. Automated KYC provides clients with a smooth experience by eliminating any back-and-forth between customers and banks when new information may be required. 

According to a 2019 study by Forbes, a total of 302 senior executives were surveyed, out of which 92% said that employee satisfaction had risen as a result of intelligent automation initiatives. 

In the new normal, the customer experience landscape is being substantially redefined across industries. A number of companies have put ML-based chatbots to better use when it comes to reducing bot-to-human interaction rate, leading to increased operational efficiency and better workforce productivity.

With the recent nod from RBI and IRDAI to Video-based Customer Identification Process (V-CIP) for Banks and FIs, Video KYC solutions too are gaining fast traction amongst businesses.

State Bank of India (SBI), via its mobile banking app YONO, has introduced a Video KYC-based account opening feature. This will allow customers to open an account with SBI without having to visit a bank branch. 

“This digital initiative powered by Artificial Intelligence (AI) and Facial Recognition Technology is a contactless and paperless process,” said SBI. 

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10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

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