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2020

AI-powered data: Is the financial industry ready?

The financial industry is the lifeblood of the national economy. With the rise of mobile internet and online payments, data has become an increasingly important asset for enterprises, and the financial industry has undergone earth-shaking changes. Especially the combination of finance and technology has enabled the financial industry's capabilities to shift from services centered around capital to services centered around data.

At the same time, the financial industry faces market saturation and competition from emerging financial services such as internet finance, posing significant challenges, especially for small and medium-sized commercial banks, securities, and insurance industries. How to leverage technology to enhance competitiveness has become the biggest challenge for financial institutions. Therefore, the financial industry is actively building modern data architectures and leveraging powerful intelligent platforms to accelerate the creation of business applications, utilize data value, and ultimately reduce operating costs and achieve precise marketing.

Data silos make data access more difficult.

The mobile internet era has greatly accelerated the generation of data (structured and unstructured). For a certain city commercial bank, this includes various environments such as Oracle, DB2, MySQL, SQLServer relational databases, and MongoDB, HBase, ActiveMQ, WebService, as well as the generation of massive amounts of data including website and APP logs, social media, videos, and images.

At the same time, because this data exists on different devices and applications, including public clouds, private clouds, and traditional databases, it has led to the creation of data silos, making it impossible to integrate data and affecting data access, making it more difficult for enterprises to obtain real analytical insights from data. For a certain city commercial bank, regardless of where the data is located, it hopes to use AI to quickly collect various types of data to form a unified data lake, making data simple and easy to access.

Lack of data governance tools makes data value mining extremely difficult.

Data silos hinder the adoption of artificial intelligence; similarly, too much data also brings data governance issues to the financial industry.

With the increase in hundreds of online and offline applications such as personal and corporate deposits, loans, credit cards, and wealth management at a certain city commercial bank, how to accurately screen effective data from massive business needs has become the core need for the bank's business improvement. Therefore, how to effectively organize, classify, and label a large amount of collected data to achieve data governance is a challenge faced by a certain city commercial bank in the data governance process. The CIO of a certain city commercial bank said, "One of the major challenges we face is the lack of an effective data governance tool to classify and label massive amounts of data, thereby enabling effective value mining of the data."

Lack of effective AI tools for data modeling.

Amid global economic uncertainty, a certain securities company needs to quickly implement data modeling and digital portraits to provide valuable information to customers. Currently, traditional data analysis systems implemented in the financial industry cannot quickly and effectively build data models to immediately combine with customer needs and services. For a certain securities company, it first lacks an AI platform that supports multi-cloud environments to quickly build an automated, more refined, rapid insight, and real-time analysis machine learning model to help the securities company analyze data in a more intelligent way. With the development and maturity of machine learning theory, how to model quickly, accurately judge, and obtain results in the first instance has become an important application trend of artificial intelligence. The financial industry particularly hopes to obtain AI tools that can quickly build accurate identification and analysis of data.

Business and data cannot be effectively connected.

For the financial industry, asset management is undergoing a major transformation. Business models are shifting from commission-based to goal-based planning support tools, making accurate needs analysis and personalized services for customers an industry need. Therefore, how to obtain customer insights through AI tools has become one of the most direct and effective means for financial asset management.

2020 First China (Shanghai) Online Industrial Products Fair

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