은행

Banks Can Prosper With Enterprise AI

Banks are facing challenges from all sides, including emerging threats from new technology-enabled Fintech competitors, stricter regulatory requirements, and pressure to simplify the client experience while simultaneously reducing costs. Implementing AI and machine learning in banking capitalizes on a once-in-a-generation opportunity for your bank to expand market share, deepen customer relationships, and compete for and win new business — all while efficiently complying with regulations and fighting financial crime.

See how banks are winning with AI.

개요 다운로드

AI와 은행

Both companies and consumers expect their banks to understand who they are, anticipate their needs, and be ready with relevant financial solutions. Banks need to deliver these solutions seamlessly across multiple channels, offering convenient access from any location, on any device. To stay competitive, you must nurture existing relationships while finding new clients in new markets. You must also compete aggressively to find the best businesses, rather than waiting for businesses to find you. AI and the application of machine learning in banking has the power to address these goals by leveraging data from your existing clients — including how their financial needs have evolved and their channel preferences.

고객 경험

Determine which client is likely to need specific products or services
Deepen your relationships with customers
고객의 니즈를 예측하고 새로운 니즈가 발생할 때 식별
적합한 목표 제안
고객에게 지원이 필요할 때 지원 확보
분석을 활용하여 고객의 가격 민감도 및 선호도 이해

대출

더욱 정확한 신용 모델 구축
리스크 조정 수익이 가장 좋은 비즈니스 발굴 및 경쟁
Actively manage your client portfolio
뛰어난 분석 기능으로 소규모 비즈니스 신용 분야 선도
Proactively intervene when clients experience financial stress
더욱 정확한 손실 예측

금융 산업

Reduce middle and back office costs related to process failures and error corrections
가격 책정을 개선하고 최고의 비즈니스 기회 포착
거래 체결 및 절차 최적화
Match investment opportunities to potential investors
적합한 고객에게 연구 보고서 제공

고객 경험
- Determine which client is likely to need specific products or services
- Deepen your relationships with customers
- 고객의 니즈를 예측하고 새로운 니즈가 발생할 때 식별
- 적합한 목표 제안
- 고객에게 지원이 필요할 때 지원 확보
- 분석을 활용하여 고객의 가격 민감도 및 선호도 이해
대출
- 더욱 정확한 신용 모델 구축
- 리스크 조정 수익이 가장 좋은 비즈니스 발굴 및 경쟁
- Actively manage your client portfolio
- 뛰어난 분석 기능으로 소규모 비즈니스 신용 분야 선도
- Proactively intervene when clients experience financial stress
- 더욱 정확한 손실 예측
금융 산업
- Reduce middle and back office costs related to process failures and error corrections
- 가격 책정을 개선하고 최고의 비즈니스 기회 포착
- 거래 체결 및 절차 최적화
- Match investment opportunities to potential investors
- 적합한 고객에게 연구 보고서 제공

은행 산업의 고부가가치 분석 과제

There are hundreds of enterprise AI applications in every function and business line in a bank. By using AI in consumer, investment, and commercial banking, your bank — whether large or small — can drive revenue growth, differentiate your brand by offering a superior client experience, reduce operational costs while improving quality, and improve risk management effectiveness and efficiency.

은행 산업 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.

은행 직무별 DataRobot 혜택:

CDO(Chief Data Officers, 최고 데이터 책임자)

Increase the productivity of your data science team.
With enterprise AI, you can get the productivity of a large data science team from a small one. Let DataRobot find the best models for you and use DataRobot’s simple deployment options to get them to market faster. Relieve data scientists from documentary requirements by using DataRobot’s automated model risk management and model validation templates.
사업부 및 업무팀 리더

Leverage AI and machine learning even if you do not have deep data science talent.
Tap into the deep expertise in your data that your bank already has. Enable business analysts and data analysts without formal data science training to build and use sophisticated models.
CTO(Chief Technology Officers, 최고 기술 책임자)

더 신속한 사용을 위해 AI 및 머신러닝 기반 솔루션을 도입할 수 있습니다.
코드 생성, Spark에 배포 및 API 기반 배포 기능을 포함하고 있는, 리스크가 낮은 DataRobot의 배포 옵션을 사용하여 더욱 빠르게 모델을 프로덕션할 수 있습니다.
데이터 사이언티스트 팀 책임자

Annihilate your backlog of analytics requests.
Let DataRobot suggest the best model in each situation, saving you the time and effort of trying and comparing every model. Use AI to build many models at the same time it takes to build one, increasing precision with more model granularity. Let DataRobot handle the low-risk models from start to finish so you can focus your talent where the payoff (or the risk) is the greatest.
CIO(Chief Information Officers, 최고 정보 책임자)

Monetize your investments in data infrastructure.
The bottleneck in many banks is no longer a lack of data. In fact, there’s plenty of data but not enough analytics staff to convert that data into actionable insights. Democratize data science with DataRobot and watch the performance of your business take off as the data reveals opportunities and improvements.

DataRobot으로 작업하기 때문에 제 모델의 내부 작동 방식을 설명하기가 이렇게 쉬울 수가 없네요.

아크쉐이 탄돈 (Akshay Tandon)
LendingTree 전략 분석 부사장
DataRobot 플랫폼을 사용하면 기존의 데이터 사이언스 방법을 사용하는 것보다 훨씬 짧은 시간에 매우 정확한 머신러닝 모델을 구축 및 배포할 수 있습니다.

Loretta Ibanez
Freddie Mac Director, Mortgage Innovation

AI가 어떻게 은행 실적을 높이는지 자세히 알아보십시오.

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