Strategic advice & alerts to leverage data analytics & new technologies
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In this article, we examine different machine learning mechanisms and propose a maximally specific conjunctive approach to fitting massive data sets in the real world of reconciliation. Furthermore, we provide a balanced solution to address the high skewness in reconciliation data sets.
There are 100 million retail investors in China. However, traditional financial advisors charge a lot, and not all investment advisors are trustworthy. For retail investors, it takes considerable time and knowledge to conduct portfolio management, and it is difficult for most retail investors to offset potential risks due to capital requirements in China. As a result, most of China’s retail investors are not able to gain secured returns in the country’s securities market.
BlueMorpho is a joint research project between InSigma Hengtian Software Ltd. and Zhejiang University in Hangzhou, China, the goal of which is to empower the legacy system modernization effort and cloud migration. My BlueMorpho colleagues and I believe that about 50%-60% of overall migration costs can be saved with the use of machine intelligence and a new methodology.
This issue focuses on key topics of interest for financial services organizations, namely equity crowdfunding, legacy systems migration, robo-advisors, test outsourcing, and refining the reconciliation process.
For financial institutions, regulatory reporting has become something of a jigsaw puzzle — one that must be cobbled together into a coherent picture from several boxes into which the pieces from different puzzles have been put over time, for an audience that will never appreciate the pain involved in organizing that picture or the time and manpower required to build it.
When it comes to IT systems in the customer care area, we keep arriving at the same idea: a trouble ticket reported by a customer and resolved in the scope of a customer care process. We organize all interactions around a process measured by key performance indicators (KPIs) and optimized for efficiency metrics, ignoring the fact that it is not process efficiency that our customers value. This Executive Update provides an alternative: a Semantic Web–based view of customer care solutions.
I predict that within the next three years the market or blockchain solutions will have progressed to the extent that the key infrastructure components will be available, security issues will be identified and mapped out, and experts available in applying the technology to specific applications and industries will start becoming available, thus providing the support for implementing and running blockchain applications on a daily operational basis.
Innovation's potential is brought to fruition only when it is complemented by back-end cloud technologies.