医学信息学论文:大数据在银行业的体现及应用

大数据在银行业的体现及应用

梁建文 香港电脑学会 会长 院士 中国建设银行(亚洲) 副行长 信息科技总监

Big (& small) Data in Banking

“No industry stands to gain more from big data than banking” “It seems like there isn’t an area that Big Data can’t help” “Powerful data processing & analytic capability is a major game-changer” “The ability to analyze pools of data used to be unreachable or unusable” “Big Data’s 5Vs: Volume, Variety, Velocity, Veracity, Value” “Big Data governance will become a big challenge”

We will look at Big Data in Banking from 6 angles:

1. Business & Customer 2. Marketing & Sales 3. Product & Channel 4. Customer Service 5. Risk Management 6. Fraud Prevention

Big (& small) Data in Banking

Know-Your-Business (KYB) Financial budget & actual performance Sales forecast & profit projection Customer gain & churn statistics Transaction patterns & trends Ratio analytics: CAR, L/D, C/I, liquidity, profitability etc. Competitive benchmarks Know-Your-Customer (KYC) Demographic & historical (7 years) data Financial & professional profiles Behavioral pattern e.g. risk aptitude, spending/shopping habit Personal financial goals Societal connections e.g. alumni, club membership

Big (& small) Data in Banking

Data-based “Digital Marketing” Analysis & Survey: Market competitiveness, Consumer trends, Propensity, Advertising & promotion Targeted, personalized & optimized offers what makes them tick, why they buy, how they prefer to shop, why they switch, what they buy next, what they’ll tell others Data-driven “Intelligent Sales” Target setting – by center, team, unit & agent Decision Science Division – campaign hot lists & scripts Hit rate tracking & reporting Customer feedback incl. praises & complaints Inquiry & issue logs analysis

Big (& small) Data in Banking

Product Development Market demands analysis – cyclical, seasonal, incidental Product trends tracking – fashion, technology, price sensitivity Benchmark & differentiation – uniqueness, 1st mover edge Life-cycle analysis – MBDL, replacements & substitutes Regulatory guidelines – PDPO, mobility, cloud, social media Channel Expansion Channels design & development Take-up rates – volume Usage tracking – velocity Effectiveness analysis – value Channel integration – variety Customer feedback – veracity

Big (& small) Data in Banking

Customer Service Personalization – customer experience & satisfaction Account & portfolio aggregation – same & cross bank Life-stages & life-style changes Event & demand-driven customer care triggers Financial services for individual, family, partners & friends Cross-sell & up-sell – wallet share, product depth Banking needs for career advance / business growth Wealth accumulation target - Investment planning Retirement planning Financial / investment portfolio performance advice

Big (& small) Data in Banking

Credit Worthiness Assessment Positive & n

egative credit checks, company search Probability & propensity of default Social media research - blogs, posts, chats, emails etc. “World-check”, “Wealth-check”, risk-check etc. Lending limits – individual, company, group, industry, country Counterparty credit positions, exposures & impacts Market Volatility Containment Asset & liability management – Interest & exchange rates forecast Hedging strategy & tracking

Operational Risk Management

System logs & alerts investigation Process improvement analysis – time, effort & cost bottlenecks

Big (& small) Data in Banking

Regulatory Compliance & Audit Regulatory reporting & compliance audit e.g. BASEL, FATCA Anti Money Laundering (AML), Counter Terrorist Financing (CTF) Fraud Prevention & Investigation ATM & e-Banking frauds – skimming, phishing, hacking records Credit card frauds – reducing loss & overhead Cyber crime – trojan, spam, internet theft Payment fraud detection & investigation Data leakage protection, detection & mitigation Fraud screening & monitoring Reduce false positives, thus lower costs

Big (& small) Data in Banking

Summary Banks have huge amount of structured & unstructured data Take Big Data as opportunities, not threats Make Big Data small & quantifiable to analyses Start with business challenges to determine analytics focuses Analytic results must be actionable & measurable Embed analytic solutions in E2E business process Migrate Big Data analytics from basic analysis to business modeling process optimization service innovation And go for some quick wins with Big Data!

Thank You