IA e o Setor Bancário
Both companies and consumers expect banks to understand who they are, anticipate their needs, and be ready with solutions. Banks need to deliver these solutions seamlessly across channels, offering convenient access from anywhere on any device. They need to deepen existing relationships while finding new clients in new markets and compete aggressively for the best business, rather than waiting for business to come to them. AI has the power to help with all of these goals by leveraging your own data about clients, how their needs have evolved, and their channel preferences.
- Determine which client is likely to need which product or service
- Aprofunde os relacionamentos com clientes
- Anticipate client needs and identify new needs as they arise
- Precisely target offers
- Ensure clients get the support they need when they need it
- Use analytics to understand client price sensitivity and preferences
- Build more precise credit models
- Find and compete for the business with the best risk-adjusted return
- Actively manage your portfolio
- Be a leader in small business credit with superior analytics
- Proactively intervene with clients experiencing financial stress
- Reduce middle and back office cost from process failures and error correction
- Projete perdas com mais precisão
- Improve pricing and capture the best business opportunities
- Optimize trade execution and routing
- Match investment opportunities to investors
- Get research reports to the right clients
High Value Use Cases in Banking
There are hundreds of AI and machine learning applications in every function and business line in a bank. With automated machine learning, banks large and small can drive revenue growth, differentiate themselves through superior client experience, reduce operational costs while improving quality, and improve risk management effectiveness and efficiency.
Check out all Banking use cases
In the world of credit, he who has the best models wins. Banks are using machine learning to build better models for estimating default probability and loss severity, and for loss forecasting. They are using these models to improve pricing for risk, credit approval, and portfolio management. Building more granular models with automated machine learning makes credit scoring more precise as models learn the nuances of discrete populations.
Fighting financial crime, especially money laundering and fraud, is more important than ever and is getting more challenging as criminals get ever more sophisticated. Using machine learning, banks are learning from their investigational findings and fraud losses and training models to accurately detect suspicious activity and to spot and prevent fraud in real time. And these models continue to get better as they learn over time.
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 machine learning to predict client needs. Some banks are identifying event triggers which may indicate that a new need has arisen. Banks are learning from client complaints where their attrition risk is highest so they can take action, and they are building predictors of traffic volume (in branch, in contact centers) so that they can staff optimally.
Banks are using machine learning 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 their clients to target their offers more precisely, an imperative with digital advertising. And they are creating sophisticated analytics to predict client price sensitivity, tailor their value proposition, and estimate price-volume elasticity.
To improve cash management, banks are using machine learning to predict new loan demand, to predict prepayment speed, and to forecast ATM cash requirements. Banks are using historical data on cash inflows and outflows to build models to predict cash flows. This allows them to have the right amount of cash on hand where and when they need it and to optimize the return on excess cash.
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 the trader’s compliance with best execution requirements.
A DataRobot Auxilia os Bancos Com:
Chief Data Officers
Increase the productivity of your data science team.
With automated machine learning, 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.
Business and Function Heads
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.
Chief Technology Officers
Bring AI and machine learning-based solutions to market faster.
Get models to production faster using DataRobot’s low-risk model deployment options, including code generation, deployment to Spark, and API-based deployment capability.
Chief Data Scientists
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 automated machine learning to build many models at the same time it took 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.
Chief Information Officers
Monetize your investments in data infrastructure.
The bottleneck in many banks is no longer a lack of data, it’s plenty of data but not enough analytics staff to turn that data into insight. Democratize data science with DataRobot and watch the performance of your business take off as the data reveals opportunities and improvements.