In the last few years, an AI virtual site visit has evolved from being a highly sought-after feature to now being a requirement for the operation of a modern-day financial services company. The main function of this implementation of AI technology is to reduce the risk of commercial credit fraud, specifically with respect to the exposure associated with unwarranted businesses originated by untrustworthy applicants in order to obtain funds from the financial services industry.<\/p>\n\n\n\n
According to the \u201cSmall Business & Medium Business\u201d report by Salesforce, about 73% of SMBs believe that they are prepared to employ artificial intelligence technology in their business. Therefore, when considering the large and technologically-savvy SMB marketplace, it is critical for financial services institutions trying to capture such a significant portion of this market to implement a current AI lending platform.<\/p>\n\n\n\n
Lenders need to develop AI-powered processes and technologies that allow them to manage risks digitally rather than through traditional in-person methods. Traditional video conferencing could be manipulated by using pre-recorded videos or fake media, thus rendering these types of communications worthless to risk-management groups.<\/p>\n\n\n\n
An AI virtual site visit that uses real-time inspection has unique digital algorithms which operate continuously in the background during all live inspections. The systems evaluate the construction site while the inspection is occurring in real-time using machine learning with local-space verification, allowing lenders to virtually eliminate any operational risks that would have existed if manual reporting were used for all branch locations.<\/p>\n\n\n\n
Remote inspection relies heavily on two essential technical aspects: advanced geo-tagging and real-time liveness detection.<\/p>\n\n\n\n
The advanced method uses precision technology to constantly monitor the real-time location of a mobile inspection device through the application of custom-built tracking algorithms, depending on connected users’ IP addresses. Incoming connections to the aforementioned mobile device are checked against the constructed data entered by users to determine whether the data includes a reference to virtual private networks or location-spoofing software used by the user when connecting to the mobile device.<\/p>\n
In cases where a mapped location matches the registered business address on both the GST and Udyam portals, this location will be verified as valid based on the requirements of the tracking system. As such, an applicant can only conduct an AI virtual site visit from within the boundaries of either their warehouse or their factory\/production floor.<\/p>\n<\/li>\n\n\n\n
Detecting if a living human is engaging with the app in person is achieved through liveness detection. By employing advanced computer vision, the AI virtual site visit analyses the live stream and assesses for anomalies such as artificial light sources, pixelation, or reflection of paper. <\/p>\n
This layer prevents fraudsters from using static images, pre-recorded high-quality videos, or AI-generated deep fake images presented through the cameras. The underlying AI models determine the variations between frames of the scanned images to establish the physical depth and texture characteristics of the human being.<\/p><\/li>\n\n\n\n
When using an AI virtual site visit, there is a strong audit trail developed by the backend system, and this makes it virtually impossible for anyone to defraud the system by setting up fictitious businesses. Lenders also form strategic partnerships with specialised healthcare and manufacturing software providers in order to optimize verification workflows that utilise the exact templates necessary to speed up the verification procedures of the various healthcare and manufacturing industries. \nThe result of this automated AI virtual site visit setup is that data can be processed almost instantaneously and no longer relies on the huge amount of local computing power that is normally required when performing manual field assessments.<\/p><\/li>\n\n\n\n
As the stream goes through its process, there are a number of specific checks happening in the background from a protocol point of view to check the video data for any possible risk indicators. This automated process also allows the engine to identify instances of multiple loan applications being originated at the same physical address or through matching device hardware fingerprints. <\/p>\n
All of these different functions provide security teams with the ability to find potential fraud networks before any money has been lent out. Additionally, if any specific anomaly is identified, then the compliance team will automatically be notified to conduct a manual audit right away.<\/p>\n<\/li>\n\n\n\n
Current virtual verification platforms link with core banking systems through a secure API connection. These cutting-edge technology linkages, known as data pipelines, provide immediate risk metrics to underwriting engines, for example, during an AI virtual site visit, eliminating the possibility of manual errors.<\/p>\n
Once a loan is set up, remote checks can immediately be triggered automatically, providing a real-world solution for completing verification securely for the client at the time of filing. Since these operational links utilise native interoperability, data can travel across systems in an encrypted form, negating any need for the manual export of files.<\/p>\n<\/li>\n<\/ul>\n\n\n\n
To make an AI virtual site visit fully functional, different elements need to be completed as one. They consist of specialised analytical tools and infrastructure that can provide large volumes of unstructured data to be converted into structured risk scores to be used in the AI in lending ecosystem.<\/p>\n\n\n\n
The computer vision models that allow for the conversion of video data into computer-readable databases are the most important element in this process. Additionally, natural language processing (NLP) tools allow for the analysis and verification of documents and paper items presented during the video visit through the use of NLP algorithms. <\/p>\n
Recently, many platforms have developed an AI chatbot to help assist the borrower with their session in real-time and provide them with live instructions regarding how and where to point their camera during the session. The chatbot is able to provide answers to the user\u2019s questions through the use of NLP, allowing the user to successfully complete the walkthrough without assistance from a human.<\/p>\n<\/li>\n\n\n\n
Large language models (LLMs) enable lenders to develop unique use cases to use the technology after a customer has visited to have their items appraised. After a visual inspection has been completed, LLMs create a comprehensive risk summary by interpreting all of the session logs collected during the visit and the automated flags from the inspection.<\/p>
In addition, AI tools can help determine whether a shop was busy or staged by looking for small visual “hotspots” as well as through audio cues during a workshop. Once the metrics are gathered in this manner from a customer\u2019s visit, the AI creates a risk summary and checks against the data found in public company registries and on confirmed social media business accounts to identify discrepancies in the information reported to the lender.<\/p><\/li>\n\n\n\n
In order to give lenders a true picture of the production facilities, many organisations are now using standard video data to generate 3D models of those facilities. The result is “virtual walkthroughs”, where underwriters back at their home locations can do virtual tours and inspect a facility as if they were in-person. <\/p>
Rather than looking at photos of a facility, the analyst witnesses an immersive experience with a full-scale map of the facility. The AI virtual site visit allows the user to view the site and identify key structural components as interactive, making it easier for the teams to assess the condition of equipment and safety. The virtual tour experience provides extreme ease-of-use and the same criteria for risk management that would be used if the team visited the site.<\/p><\/li>\n<\/ul>\n\n\n\n
Consider a major Indian non-banking financial corporation (NBFC) that was facing severe problems with fraud and delinquency rates in its unsecured MSME portfolio. Manual verification processes resulted in long delays of seven to ten business days for loan approvals and ultimately resulted in high-quality borrowers exiting the NBFC. Additionally, forensic audits revealed that almost 12% of defaults were attributed to organised fraud, where fake rental receipts were being used by the fraud rings.<\/p>\n\n\n\n
To solve the issues, the NBFC’s risk management team developed and deployed a dedicated AI virtual site visit workflow for all applications. Additionally, they integrated an AI-powered verification platform that worked directly inside a web browser without requiring the borrower to download an app.<\/p>\n\n\n\n
The operational results were immediate and structurally transforming:<\/p>\n\n\n\n
Introducing an AI virtual site visit has totally reinvented how commercial lenders do business in India. By integrating sophisticated geolocation with multidimensional \u2018living-ness\u2019 verification, financial services firms can now shield their portfolios from fraudulently submitted businesses. As a result, previous operational constraints can be removed, enabling nimble lenders to safely take advantage of the fast-growing MSME sector. So, what are you waiting for? Contact an integration team today to implement an AI virtual site visit specifically designed for your risk management processes.<\/p>\n\n\n\n
Advanced geographic tagging is built into the system. The moment a VPN is utilised, the system will detect the inequities of the network, leading to a mismatch in geographic location and a discrepancy with data. The session will be automatically stamped as “high-risk”, which will result in the halt of the verification process.<\/p><\/li>\n\n\n\n
With the use of computer vision for dynamic liveness detection, the system confirms that a live stream is coming from an actual physical environment by examining the frames of video being streamed in live motion for specific characteristics. For example, the system will evaluate these areas for depths, reflective surfaces, and edges to determine if the stream is from a live environment. If the subject attempts to use high-quality video or photo clips in a loop format on the lens of the camera, it will be classified as fraud.<\/p><\/li>\n\n\n\n
Yes, current technological advances for verification software have created means for dynamic data packet optimisation based upon the actual speed of the networks from moment to moment. The media engine performs real-time video resolution determination based on these two variables in order to minimise dropped frames while ensuring that the associated geographic metadata associated with the session is preserved.<\/p><\/li>\n\n\n\n
Lending institutions can deploy these verification capabilities quickly because of their ability to connect to cloud services that couple verification functionality with existing systems. Users can perform the entire facility verification process via standard web-based links rather than having to load any large, non-web applications onto their devices.<\/p><\/li>\n\n\n\n
This verification process through an AI virtual site visit essentially creates a secure data pipeline to deliver reliable environmental risk data to your core underwriting engines. By converting unstructured video data into structured asset files, the verification process permits automated underwriting systems an objective method to validate the existence of a physical asset.<\/p><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"