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
Applications for machine learning for banking are as varied as the banking business itself. At dotData, we have worked with some of the world’s largest financial institutions and are ready to help you make the most from your AutoML investment.
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.
How dotData Powers AI in Banking
The data center for the modern bank is quickly becoming one of the most sophisticated and diverse of any industry. dotData provides a rich set of solutions that will provide immediate benefits to all impacted parties:
Chief Data Officers
Monetize your data and get projects out of the lab
Leverage automated machine learning and data science automation for AI in banking to boost the productivity of your data science team. More than 80% of the work performed by your data scientists happens before the machine learning modeling, and dotData is the only AutoML solution that can help automate the entire life-cycle, rather than optimize model selection and optimization. Whether it’s accelerating data processing, feature engineering, model selection, or even documenting model selection and integration into production environments, dotData can help shorten months-long projects to days.
How Sumitomo Mitsui Banking Corporation Accelerated MI & ML Using AutoML 2.0
Download our case study to learn how Sumitomo Mitsui Banking Corporation Accelerated their ML and AI development process using dotData’s AutoML 2.0 Solution.
READ THE CASE STUDY TO LEARN:
How a lack of data science resources drove the search for an alternative
Why dotData was chosen over 300+ vendors
The role of AI-powered feature engineering in automating ML & AI
Line of Business Users
Finally get ROI from your AI projects
Whether your bank already has a deep data science team or is leveraging a minimal team size, getting the most benefit from your AI in banking and machine learning investment can be slow and painful. With more than 95% of AI projects never leaving the lab, justifying ROI for AI and ML projects can be frustrating. dotData helps accelerate your data science initiatives and enables a new class of data scientists by allowing your Analysts and BI teams to create AI and machine learning models without having to become full-fledged data scientists.
Chief Technology Officers
AI technology that stays out of your way
Deploy AI in banking in record time and allow for near real-time updates to production models using automation and API-based integration. dotData helps you make the most of your AI investment with an API-based approach that makes operationalization fast and straightforward while giving you a seamless means of providing real-world usage feedback to help modify and optimize models without having to re-deploy them.
Chief Data Scientists
The tools your team loves, without headaches
Leverage dotData’s award-winning automated machine learning and data science automation technology to accelerate AI in banking across your entire life-cycle. Use dotData to provide rapid feature engineering that will accelerate your ML model development from months to a few days. Leverage automation to expedite the development of low-risk models while giving your data science team the freedom to focus on higher-pay off projects. Deploy the right solution for the right organizations with a data science automation platform that can be deployed either as a GUI-based solution or integrated into your favorite coding environment.
Chief Information Officers
Data science democratization is finally here
More than 96% of data science projects never leave the lab. For most modern CIOs, the problem is no longer a lack of data; it’s how to best leverage that data and how to identify the right resources that can help accelerate usage. Deploy the technology that Forrester called “…AI’s best-kept secret…” and watch performance and business unit satisfaction take off. Truly democratize the data science process by enabling a new class of users, while providing your data science teams with the tools they love in the coding environments they love.
AutoML 2.0: Data Science Automation Accelerates Your Business
Scaling a data science practice is challenging, time-consuming, and expensive. With Automated Data Science, you can empower data analysts, software engineers, and BI professionals to build and benefit from predictive models. Through Data Science Automation, you can embed models into applications seamlessly, while freeing up the time of your data science team to be more productive.
BI & Data Analysts
Unlike traditional AutoML systems that require users with in-depth knowledge of data wrangling and constructing feature tables, dotData automates 100% of the data science process. With dotData and minimal training, your BI team and data analysts can quickly learn to contribute to your ML and Enterprise AI initiatives, freeing precious resources and accelerating time to market for your AI & ML initiatives.
Data scientists spend 80% of their time in wrangling with data and constructing complex feature tables. By automating the entire workflow, you can liberate your data science team from the mundane tasks associated with data science and give them the power to provide tangible value to your business in a scalable, seamless manner that is not possible with hand-coded approaches or traditional AutoML platforms.
IT & Software
Integrating your AI and Machine Learning models into production environments is a crucial step in deriving value from your Enterprise AI projects. dotData gives your IT and engineering teams a seamless API-based integration model that enables Continuous Deployment and makes deploying and maintaining models fast and straightforward.
Executives and Line of Business
Giving executives and line of business leaders insights into the Machine Learning and Enterprise AI process provides the transparency and line of sight needed to keep projects moving and provide discernible ROI and value for the organization.