For me, the most impressive lecture in Wall Street Trek is Leveraging Big Data in Asset Management by Andrew Chin. He talked about the on-going use of big data in asset management, and the future trend. Also, he talked about the opportunities and challenges in leveraging big data to 1) impact investment decisions, 2) improve sales productivity, 3) enhance client interactions, 4) create operational efficiencies. Asset managers will need to develop strategies to use this data. Also, there is likely a continued demand for professionals with data science skills.
As I am working as a risk management consultant for banks, I think what Andrew’s points of view can also be leveraged in the banking industry. Big data is leading a revolutionary change in Chinese banks.
(1) Data will become the core competitiveness of the bank
Now more customers use e-banking and mobile terminals, banks have recorded a very rich location, behaviour preferences, demand preferences and other information, a large number of information waiting for data analysis and mining. For banks, people, counters, technology in the next period of time are substitutable, only the data is a long-term accumulation, and an irreplaceable key factor. If a bank cannot leverage data as a strategic asset to be developed, the bank will be in a backward position in the fierce market competition in the future, or even lose its core competitiveness.
(2) Typical data application in the banking sectorn
A. Customer
Through the comprehensive collection and integration of internal and external customer data, a complete customer analysis dashboard can be formed to forecast customer needs, and integrate the whole line of products and service resources, so as to provide comprehensive services to our customers.
B. Risk management
The use of customer transactions, industry trends, regional environment, business owners information and other information help banks to find the potential risks more timely and accurately.
(3) Risk management data analysis status quo of Chinese banks
Chinese banks are aware of the importance of data analysis in risk management, especially in the retail and credit card, etc. The banks have carried out many useful attempts, and already made some achievement.
In the field of risk management, most banks have carried out a series of big data application such as scorecard and risk measurement, but there is still room to be improved for risk forecasting and early warning. Traditional risk measurement relies more on financial data of lenders, and these data is often lagging behind. Especially for small and micro enterprise lenders, there is big problem of using financial data for risk control. Big data is useful in this regard. And the value of big data in risk management can be further excavated.
(4) Examples of big data application in risk management
Small Business Risk Management
Due to the poor quality of financial data of small enterprises, the risk management of small enterprise credit business is more dependent on the behaviour data of small business owners. By analysing the behaviour of small business owners through big data analysis, it helps to evaluate the probability of default so as to improve the risk management of small business.
Anti-fraud in Loan Application
Linking customers, accounts, behaviours etc. through social network analysis (SNA), such as sharing phone numbers, transferring money, etc., identifying potential affiliates and illegal intermediaries to prevent repeated lending and organized fraud.
Overall, big data is playing an increasingly important role in banking sector, and just like Andrew said, we should always pay attention to the challenge of using big data.
