现货投资对渠道影响等问题进行碰撞

admin/2019-12-03/ 分类:配资平台/阅读:
Recently, McKinsey released the China banking CEO quarterly (winter 2019) (hereinafter referred to as the report).
It is pointed out that in recent years, in the face of external operating pressure, competition and regulatory changes, domestic banking revenue and profit growth has been difficult to break double digits;
But in the same tough environment, the world's leading Banks can still achieve 10-15 per cent growth in their corporate and retail core businesses through big data applications.
现货投资看完下文就知道了!
In addition, in response to the high rate of non-performing loans plaguing many Banks, leading Banks have used big data and artificial intelligence technology to achieve good risk control despite the macroeconomic downturn.
While rising labor costs and increased investment in science and technology have increased the cost/revenue ratio of many Banks, digitalization, big data and artificial intelligence can help Banks effectively reduce costs and increase efficiency.
 
According to the analysis of McKinsey global data workshop, large-scale application of big data and advanced analysis can significantly improve banking performance, reduce operating costs, optimize risk control and decision-making, improve the efficiency of regulatory data and improve customer experience.
The huge amount of money Banks are spending on big data and advanced analytics is just seeing the value behind it.
McKinsey global institute (MGI) estimates that advanced analytics has a value creation potential of $95,000 to $15.4 trillion across industries globally, boosting banking profits by 10% to 15%.
 
Thanks to the internal requirements for high-quality development of China's banking industry, the state policy support and the increasingly mature technical capabilities, the scale of big data and advanced analysis has entered the golden age in China's banking industry, which is the general trend for the future development of the whole industry.
 
"More than 90 per cent of the world's top 50 Banks are actively using advanced analytical techniques," according to qu xiangjun, global senior managing partner and head of McKinsey's financial institutions advisory practice in China.
The world's leading Banks invest 15-17 per cent of their pre-tax profits in digitisation, technology and big data, with technology and big data staff accounting for about 17 per cent of the total.
That compares with less than 5 per cent of relevant talent in China's banking sector.
It has been the consensus of the whole industry to embrace big data and improve the competitiveness of Banks. Financial institutions that can take the lead in transforming into 'technology Banks' and' data Banks' will lead the industry in the next 10 years."
 
However, it should be noted that at the present stage, domestic Banks still have several common problems in terms of big data scale.
Smaller Banks are more fearful, with poor data bases that keep them on the sidelines.
In addition, it is common for all kinds of Banks to apply "two skins" in model building and business scenarios, not following the principles of "use case driven" and "closed-loop loop optimization".
The lack of big data talents further restricts the large-scale application.
  Operation timed out after 30001 milliseconds with 0 out of -1 bytes received着眼于“技术授权”问题,很容易从首席执行官的角度来准备首席执行官张克,小伞总裁徐涵,i Yunbao总裁,李哲和保险总监陈晓。客户分别展示客户服务,渠道服务和风险。科学技术在控制中的核心力量。
  在圆桌对话中,新泰市保险发展规划部总经理刘跃,宜信博城总经理孟凡进,鼎鼎保险总裁朱水明,黎明保险总裁郑伟,兴恒保险公司临时总裁于胜熙和恒邦保险公司总裁胡玉浩在中介公司面前,就诸如中间市场的未来发展趋势,互联网平台对渠道的影响等问题进行了碰撞和讨论。中间渠道的中间和未来产品趋势。
  在本次论坛上,保险中介市场上最具影响力的行业精英在南京开会,就中介渠道的未来发展前景和趋势分享了他们的看法和想法,共同探讨了战略和发展模式。频道的不同观点和概念的碰撞为市场参与者提供了对其未来发展定位的更加清晰的理解,也有效地促进了不同公司之间的交流与合作。健康有序的发展在促进中发挥了积极作用。
  聚在一起,共赢未来。机遇与挑战并存于2020年。与会的中介公司已宣布,他们将通过更具战略性的合作政策和模式来推动中介机构的发展,向前迈进,为所有人创造有利的局面,并为中介市场创造更美好的未来。行业!
In response, the report gives three strategic initiatives.
First, develop a value-driven big data implementation roadmap: identify the bank's opportunity points through big data diagnosis, define and prioritize big data use cases, develop the best implementation roadmap, and reach consensus across the whole bank;
Secondly, end-to-end big data use case pilot: through landing 1 or 2 pilot use cases, we closed the loop of end-to-end big data use cases, rapidly iterated and optimized use cases, verified their business value, and achieved quick win.
Finally, consolidate the supporting system and accelerate the large-scale implementation of big data: for a bank with an information technology budget of $1 billion, simplifying, sorting out and optimizing data management can save $71 million annually.
Banks should gradually establish centers of excellence for big data within 18 months, recruit and train core talents for big data, improve data governance mechanism, and build systems related to big data, which is the most important thing to ensure the large-scale implementation of big data.
 
Qu xiangjun finally concluded: "first, the application of big data in Banks should start from 'small data', start from the internal data of Banks, generate value through analysis, and realize small steps and quick runs;
Second, a top-down use-case driven approach is more effective than a bottom-up approach to data cleansing.
Third, data cleansing is a continuous project, not a one-off;
Fourth, big data use cases should be scaled, from highlights to scale, and finally build the platform;
Fifth, the cultivation of big data talents should be large-scale. Banks should establish talent centers and big data colleges to realize the batch cultivation of talents.
Sixth, the establishment of a federal organizational structure, in the front business department and the department of science and technology should be equipped with data analysts, so as to achieve the integration of business, data, science and technology;
Seventh, bank decision makers need to develop a data-driven (IBS) decision-making culture that integrates data into the bank's DNA."
以上就是小编为您整理的关于本页面的资讯,资料来源于网络,如有不妥请您见谅,如需了解配资方面的知识与咨询,请关注本站,恭候您的光临。
阅读:
扩展阅读:
老湿财经新闻自媒体 www.qxnic.com 老湿网邮箱:laoshicaijing@qxnic.com
二维码
意见反馈 二维码