From powering self-driving cars to enabling facial recognition on smartphones, CV has driven numerous innovations. It involves the use of advanced algorithms and AI techniques to interpret and recognize patterns in images and videos, allowing for the automation of complex tasks. According to Business Insider, a global news website, 90% of banks recognize the benefits of AI solutions like computer vision, with 75% investing significantly in implementation. TCS research indicates that AI investments can reduce production costs by 13% and increase revenues by 17%. Since its inception in the 1960s, computer vision has evolved from basic optical character recognition (OCR) to sophisticated, automated image analysis, driven by deep learning and enhanced computing power. Today, it is applied across various sectors, making tasks more efficient and accessible. In the banking sector, the ability of this AI empowered technology to "see" and analyse visual data presents significant opportunities for cost savings, enhanced operational efficiency, and improved customer service, as detailed in this blog. Computer Vision Use Cases in Banking 1. Fraud Prevention Behavior Analysis: CV plays a critical role in enhancing security for banks. Real-time surveillance and video footage analysis allow for the early detection of suspicious behaviours within bank branches, helping to prevent fraud before it escalates. Document Verification: Advanced algorithms verify the authenticity of documents, reducing risks of forgery and identity theft through techniques such as Convolutional Neural Networks (CNNs) and feature extraction methods like SIFT and HOG. Anomaly Detection: Integration of visual and transaction data enhances accuracy in identifying fraudulent activities. 2. Customer Service Facial Recognition: Identifies VIP customers upon entry, allowing for personalized service offerings. Gesture Recognition: Automated kiosks enable intuitive interactions for improved customer engagement. 3. Document Verification: CV Streamlines the verification of documents (e.g., passports, driver’s licenses) by extracting and analyzing text and images, which reduces manual errors and enhances onboarding efficiency. 4. Check Deposit Remote Deposit Capture: Mobile banking apps facilitate check deposits by allowing customers to capture images of checks, improving convenience and processing speed. 5. Enhanced Branch Experience Customer Identification: Cameras at entry points match customer images with stored records for personalized service. Sentiment Analysis: Analyzes facial expressions and interactions to gauge customer sentiment and improve service delivery. 6. Commercial Banking Advanced OCR Tools: Utilizes computer vision-based OCR (e.g., Amazon Rekognition, Google Vision API) to automate document classification and data extraction from unstructured documents, significantly improving accuracy over traditional OCR systems. 7. Supporting Cashless Transactions Cardless Technology: Initiatives like Wells Fargo's cardless ATM transactions enable customers to perform transactions using mobile apps instead of physical cards, promoting digital payment adoption. 8. Claims Processing and Underwriting Image Recognition for Insurance: Fintech applications can assess vehicle damage through image analysis, facilitating efficient claims processing by generating repair estimates and influencing premium adjustments. Satellite Imagery Analysis: Platforms like Cape Analytics analyze properties to identify potential damage or quality issues, enhancing underwriting decisions for insurers. The below table outlines various focus areas in banking, categorized into three main themes: Fraud Prevention, Data Extraction from Physical Documents, and Customer Experience. Best Practices in Banking Implementing computer vision in banking requires caution, focusing on data privacy and security through encryption and access controls, mitigating bias with diverse datasets and regular model audits, and ensuring regulatory compliance with data protection laws like GDPR and AI ethics. Challenges Despite advances, computer vision faces challenges such as the difficulty and cost of acquiring high-quality, diverse datasets; the significant computational resources required for training models; ethical and privacy concerns regarding biometric data; and the need to minimize false positives to maintain customer satisfaction. Effective computer vision systems depend on large volumes of labelled images for accuracy, necessitating extensive training to achieve reliable results. These process can take years without adequate investment in development. For more insights on these technologies, you can consult our experts at 10xDS, a market leader in AI and computer vision solutions. Looking Ahead Computer vision is evolving rapidly, offering the ability to analyze millions of images quickly and in detail, maintaining high accuracy. This technology has significant potential for banks to enhance their operations and embed it into their value chains effectively. To unlock value through cost savings, improved customer experiences, and enhanced fraud control, Banking and Financial firms should integrate computer vision into their digital transformation strategies. Before implementation, they must carefully evaluate software options, ensure compliance with privacy regulations like the General Data Protection Regulation, and consider transitioning to cloud platforms. Given the complexities of adoption, partnering with a service provider after thorough market analysis is advisable. Future research in computer vision will likely focus on increasing efficiency by developing lightweight models that require less computational power without sacrificing accuracy. Exploring unsupervised and semi-supervised learning methods could also help reduce dependence on large, annotated datasets. Addressing ethical concerns and creating models that can explain their decision-making processes will be crucial for wider acceptance and application of computer vision technologies.