Digital currencies' fast growth has transformed financial transactions, creating possibilities and concerns. This chapter examines multimodal digital currency financial risk assessment and fraud detection strategies. The multimodal strategy uses several data sources and analytical methods to detect and mitigate financial dangers. The multimodal technique combines transaction data, behavioral analytics, machine learning algorithms, and blockchain technology. Financial firms may identify normal and unusual behavior using transaction data. Behavioral analytics improves this process by analyzing user behavior to identify fraud. The multimodal technique relies on machine learning algorithms to discover abnormalities and anticipate dangers in massive datasets. These algorithms may adapt to new fraud patterns, keeping detection systems strong against shifting threats. Blockchain technology makes digital money transactions more transparent and traceable, making fraud harder to hide. The chapter also addresses multimodal technique difficulties such data privacy, real-time processing, and data source integration. The regulatory environment and compliance's role in implementing these technologies are also examined. This chapter details how multimodal approaches might improve financial risk assessment and fraud detection in digital currency, securing digital financial systems.