Enhancing Financial Approvals with AI-Powered Predictive Automation: Optimizing Invoice Management and Vendor Risk Assessment (Published)
This article explores the transformative potential of AI-powered predictive automation in enterprise financial approval processes. By leveraging advanced machine learning models trained on historical vendor data, organizations can implement intelligent systems that classify invoices based on rejection likelihood, streamlining workflows and reducing manual intervention. The predictive capabilities enable automatic processing of low-risk vendor invoices while flagging higher-risk submissions for thorough review. This article addresses traditional inefficiencies in financial document processing, offering significant benefits including accelerated approval timelines, reduced operational costs, enhanced compliance, improved accuracy, and substantial productivity gains. The integration of these predictive analytics capabilities represents a strategic advancement in financial operations management, positioning enterprises to achieve sustained improvements in both efficiency and financial governance.
Keywords: financial workflow optimization, invoice classification, machine learning models, predictive automation, vendor risk assessment
AI-Powered Personalization in Retail: Technical Implementation and Business Impact (Published)
This comprehensive article examines the transformative impact of artificial intelligence on retail personalization strategies. The article explores the technical architecture underpinning AI-powered retail systems, including data collection infrastructure, processing pipelines, and specialized machine learning models that enable personalized customer experiences. It addresses implementation challenges like real-time processing requirements and cold start problems while detailing key business applications such as intelligent product recommendations, dynamic pricing optimization, personalized marketing automation, and conversational commerce. It evaluates business impact across revenue metrics (conversion rates, order values, customer lifetime value), operational efficiencies (marketing costs, inventory management, return rates), and customer experience indicators. Ethical considerations including data privacy compliance, algorithmic fairness, and transparency practices are thoroughly examined. Finally, the article identifies emerging technologies shaping the future of retail AI, including computer vision applications, voice commerce integration, and augmented reality experiences. This synthesis of technical implementation and business outcomes provides stakeholders with evidence-based insights into the strategic value of AI personalization in contemporary retail environments.
Keywords: Customer Experience, artificial intelligence in retail, data privacy ethics, machine learning models, personalization strategy
Machine Learning and Deep Learning Models for Predicting Mental Health Disorders and Performance Analysis through Chatbot Interactions (Published)
Mental health disorders have recently been prompting increased concern globally and finding new ways of diagnosing and treating them efficiently. Machine learning (ML) and deep learning (DL) enabled chatbots are enormous tool for predicting and supporting mental health. This work aims to carry out an assessment of several AI models for prognostics of mental health disorders based on the comparison of intents, patterns, and responses in a structured chatbot-based dataset. Since it is intent-based, our dataset is best suited to classifying user inputs accurately into different mental health thematic buckets such as anxiety, stress, and proved suicide ideation. To assess the models, we compared basic models such as Multinomial Naïve Bayes, Random Forest and SVM as well as deep learning models including LSTM networks. SVM and LSTM showed promising results among the tested models with the accuracy of 94.6%. LSTM was proved to address the problem of sequential context dependence typical for conversational data. For further improvement in the model’s accuracy, we used ensemble methods whose accuracy came out near like the highest accuracy models, 94.2% accurate. This work is new in the sense that it involves the use of data from an intent-based chatbot, and a comparison of the ML and DL models designed specifically for the prediction of mental health outcomes. Also, it is important to note that we dealt with underrepresented intents, including suicide ideation, using data augmentation and ensemble approach. It fills the gaps in the deployment of AI for mental health by providing recommendations concerning the model’s performance and possible ethical concerns as well as integrating it into conversational assistance. We also found the relevance of an AI chatbot in the delivery of efficient and easily deployable intervention for mental health.
Keywords: Mental health prediction, chatbot-based AI, deep learning algorithms, intent classification, machine learning models