Longer-term projections indicate that today’s developing and rising nations will account for roughly 60% of the global GDP by 2030. There is tremendous financial growth and advancement in developing countries, resulting in a high demand for personal loans from citizens. Depending on their needs, many people seek personal loans from banks. However, it is difficult for banks to predict which consumers will pay their bills and which will not since the number of bank frauds in many countries, notably India, is growing. According to the Reserve Bank of India, the Indian banking industry uncovered INR 71,500 in the scam in the fiscal year 2018–2019. The average lag time between the date of the occurrence and its recognition by banks, according to the statistics, was 22 months. This is despite harsher warnings from both the RBI and the government, particularly in the aftermath of the Nirav Modi debacle. To overcome this issue, we demonstrated how to create a predictive loan model that identifies problematic candidates who are considerably more likely to pay the money back. In step-by-step methods, we illustrated how to handle raw data, remove unneeded portions, choose appropriate features, gather exploratory statistics, and finally how to construct a model. In this work, we created supervised learning models such as decision tree (DT), random forest (RF), and k-nearest neighbor (KNN). According to the classification report, the models with the highest accuracy score, f-score, precision, and recall are considered the best among all models. However, in this work, our primary aim was to reduce the false-positive parameter in the classification models’ confusion matrix to reduce the banks’ non-performing assets (NPA), which is helpful to the banking sector. The data were graphed to help bankers better understand the customer’s behavior. Thus, using the same method, client loyalty may also be anticipated.

Nowadays, finding genetic components and determining the likelihood that treatment would be helpful for patients are the key issues in the medical field. Medical data storage in a centralized system is complex. Data storage, on the other hand, has recently been distributed electronically in a cloud-based system, allowing access to the data at any time through a cloud server or blockchain-based ledger system. The blockchain is essential to managing safe and decentralized transactions in cryptography systems such as bitcoin and Ethereum. The blockchain stores information in different blocks, each of which has a set capacity. Data processing and storage are more effective and better for data management when blockchain and machine learning are integrated. Therefore, we have proposed a machine-learning–blockchain-based smart-contract system that improves security, reduces consumption, and can be trusted for real-time medical applications. The accuracy and computation performance of the IoHT system are safely improved by our system.

As Internet of Things (IoT) technology develops, medical equipment, wearables, sensors, and users can be linked together to create an ecosystem known as the Internet of Medical Things (IoMT). IoMT enhances the effectiveness, precision, and affordability of the current healthcare system. It is difficult to guarantee data security and privacy of data in the IoMT ecosystem due to the fact that it incorporates several businesses, heterogeneous networks, and a lot of private information. IoT devices' processing and power requirements make it difficult to ensure their security. In order to secure their data and connections, many IoT systems use asymmetric cryptography and Elliptic Curve Cryptography (ECC). Implementing and deploying these security and privacy solutions for the IoT environment, however, continues to be a substantial problem due to the hardware restrictions of IoT objects. In this paper, we have presented a more effective lightweight cryptographic system based on Elliptic Curve and Dual Cryptography (ECDC) to strengthen ECC and secure communication between IoMT devices. It protects against all kinds of security problems, like authentication, privacy, and integrity, and stops common cyberattacks.