<p>Financial systems today exist in a very dynamic environment, with huge amounts of data being created, non-linear financial market dynamics and an expanding threat from cyber attacks. Traditional machine learning models and classical cryptographic models have many difficulties to deal with large dimensional financial data sets, the large number of optimization steps and the emergence of new threats from quantum computing. There is need for models that will improve the predictive power, reduce the cost of optimization and ensure the long term security of financial transactions. An Integrated Framework Of Quantum Enhanced Financial Analytics. This paper proposes an integrated framework of quantum enhanced financial analytics (QEFA), based on five quantum-enhanced computational techniques: Quantum Support Vector Machines (QSVM), Quantum Approximate Optimization Algorithm (QAOA), Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBMs) and post-quantum lattice-based cryptography. QEFA aims to leverage the predictive power of QSVMs, the optimization power of QAOAs, the dimensionality reduction of QPCAs, the forecasting power of QBMs and the post-quantum security of lattice-based cryptography to provide a comprehensive framework for financial decision makers to make better decisions. The proposed QEFA framework works as a multi-step analytical pipeline, which processes large-scale financial data sets. It first applies QPCA to perform dimensionality reduction on the high dimensional financial indicators (e.g., stock prices, trading volumes and macroeconomic indicators) for the process. The resulting low dimensional data set is then fed to QSVM for performing market anomaly detection and classification. QBMs are applied to perform time series forecasting of financial indicators in process. Finally, QAOA is employed to optimize portfolio allocations. Post-quantum lattice-based cryptography is used to secure financial transactions against both classical and quantum computational attacks.</p>