은행 산업의 고부가가치 분석 과제

• 신용 (Credit)

  In the world of credit, the best models win. Banks are using AI to build better models for estimating default probability and loss severity, and for loss forecasting. These models help improve pricing for risk, credit approval, and portfolio management. Building more granular models with enterprise AI also makes credit scoring more precise, as models learn the nuances of discrete populations.

• 금융 범죄

  As criminals get more and more creative with their tactics, banks face increased pressure to stay ahead of bad actors when fighting financial crime, especially money laundering and fraud. Using AI, banks are learning new insights from their investigational findings and fraud losses and training models to accurately detect suspicious activity or to spot and prevent fraud in real time. And these models continue to get better over time as they learn new types of malicious activity.

• 고객 경험

  Clients expect banks to know who they are, what they need, and when they need it. Drawing from data on clients in similar situations, banks are using our enterprise AI platform to predict client needs. Some banks are identifying event triggers which may indicate that a new need has arisen. Reviewing client complaints, for example, can indicate where your bank’s attrition risk is highest and prompt you to take action. These insights also help you to build predictors of traffic volume (in branch, in contact centers) so that you can staff each unit appropriately.

• 마케팅

  Banks are using our enterprise AI platform to predict which prospects are likely to become the most profitable clients and are using this ability to prioritize leads and referrals. Banks are learning from clients to target their offers more precisely, an imperative with digital advertising. Your bank can use sophisticated analytics to predict client price sensitivity, tailor your value proposition, and estimate price-volume elasticity.

• 현금 관리

  To improve cash management, banks are using enterprise AI to predict new loan demand, to anticipate prepayment speed, and forecast ATM cash requirements. Banks are using historical data on cash inflows and outflows to build models to predict cash availability. These insights enable your bank to have the right amount of cash on hand where and when you need it and to optimize your return on excess cash.

• 글로벌 시장

  In financial markets, traders are using historical transaction cost analysis (TCA) and execution data to build models that optimize order routing and trade execution strategy. These models evaluate the relative merits of the numerous potential algorithmic trading approaches, venues, and counterparties. These support trader decision-making and help to minimize market impact and cost while demonstrating and recording your efforts to fulfill execution requirements.