In the world of banking, the difference between success and failure ultimately comes down to managing risk. Banks face constant pressure from competitors, growing governance and regulatory requirements, increasing costs, fraud, customer defaults, and more. At the same time, banks face growing competition and a growing need to provide more exceptional and better customer service while staying within regulatory frameworks and staying ahead of crime and defaults.
AI in Banking: AutoML 2.0 to the Rescue
How AI Helps Banks
Whether it’s commercial banking or consumer lending, wealth management and banking, your customers expect you to know who they are, anticipate their needs, and have tailored solutions that apply to their unique use-cases. To deliver services and financial vehicles seamlessly across multiple channels and with universal access, you need to be able to leverage your available data to predict how client needs are evolving, what products and services are most likely to be beneficial and their preferred method of interaction. Banks can leverage the power of AI in banking, powered by data science acceleration, to optimize their client portfolio offerings.
AutoML and Data Science Automation can help banks deploy AI in banking to optimize their customer experience:
- Product Targeting: Precisely predict customer profiles for specific products and services
- Cater to customer needs and deepen relationships
- Anticipate client needs and identify new opportunities
- Precise targeting of new services
- Optimize your customer support experience
- Uncover client preferences and sensitivity to price changes
Customer lending can be a high-risk proposition, even under the best of circumstances. Use AutoML and Data Science Automation to create a better value proposition
- Create precise credit models that analyze risk
- Identify your optimal business based on risk-adjusted returns
- Manage your lending portfolio for maximum return
- Identify clients with financial stress and intervene proactively
- Create loss-forecast models that mitigate risk
Managing your financial portfolio requires diligence and deep insights into market changes and opportunities. AI and Machine Learning can help you spot key trends to optimize returns.
- Optimize the execution and routing of trades
- Match investment opportunities to investors
- Analyze market conditions and spot key trends
- Reduce transaction costs by minimizing errors
Use Cases for AI in Banking
Credit Monitoring & Management with AI
In the world of banking, managing credit properly can be the difference between profitability and loss.
Banks use machine learning models to understand the factors that lead to defaults and those associated with loss severity and forecasting. Using these models can help create a more balanced approach to pricing, credit approval, and portfolio management that provides the best results for clients while managing risk for the bank. dotData helps you build granular models using AutoML and Data Science Automation in record time.
Monitor Fraud & Financial Crime with Machine Learning
Fraud and financial crime monitoring is a critical part of managing the safety of your bank.
Whether it’s identifying the patterns of money laundering or preventing fraud, staying ahead of criminals is becoming harder and more expensive. Banks use machine learning to leverage data from previous investigations to create models that accurately detect suspicious activity and can raise flags for further investigation in real-time. dotData helps create these models and allows you to continuously improve them with our unique Continuous Deployment feature, based on future learnings.
Improve the Banking Client Experience with AI
The modern banking client is more discerning and discriminating.
They expect their bank to know everything about their needs and to be present with solutions when they need them. Use machine learning and AI in banking to predict client behavior and demands and leverage your data to predict client need triggers. Leverage client data about satisfaction and especially complaints to better predict at-risk clients to take action to prevent attrition. Build models for predicting branch traffic volumes to identify fundamental seasonal and permanent staffing needs and to understand where and when new branches are needed.
Manage Customer Acquisition for Banks with AI
Banks are using AI and machine learning to model customer acquisition patterns and to optimize marketing spend to ensure the highest ROI possible.
Leverage historical client data to pattern ideal target customers, predict buying behaviors and identify pricing sensitivity criteria so you can tailor offers and create products that are better suited to your target audience. Leverage AI and machine learning to define marketing channels that are most likely to yield optimal results and optimize your digital and traditional media spend using predictive models.
Forecast Financial Product Demand with ML & AI
Understanding what products you will need, in what markets and at what time is a critical part of operating a profitable modern bank.
AI in finance and machine learning can help you forecast demand for financial products, loan-types, mortgage rates as well as to understand cash flow requirements both on an organizational as well as at a branch – even the ATM – level.
Optimize Financial Investments with ML & AI
Understanding and managing investments in the modern economy can be precarious and time-consuming.
Leverage historical transactional cost analysis and execution data to build models that optimize trade routing and execution. Build models that measure the relative validity of execution models, venues, and potential trading parties. Using AI in banking, financial institutions can create modern decision-support systems that optimize market impact while ensuring compliance.
The Right Product for the Right User
Start by selecting the product you need, based on your environment, your use-case and your need to “get dirty” with the details of your data science workflow.
AutoML 2.0 & Data Science Automation
Leverage a full GUI experience to automate as much of your data science workflow as necessary. Empower citizen data scientists and data scientists alike.