AI Adoption and the Financial Services Industry
Financial Services are under pressure from all sides; There are threats from new disruptive technology-enabled competitors, increasing regulatory requirements, the pressure to simplify the client experience and, at the same time, reduce costs.
AI and machine learning represent a once-in-a-generation opportunity for FS companies to gain market share, deepen relationships, and compete for and win the best business, while efficiently complying with regulations and fighting financial crime.
The major issue has been that the most insightful data in FS is unstructured. Most CRM systems cannot manage unstructured data, which means that all of that insight is going to waste.
What if you could enhance your knowledge workers by enabling them to utilise all of the data, all of the time by recognising context, relevancy, intent and interest thus delivering vastly superior prediction and search results whilst delivering it to them automatically through a cognitive understanding of their interests and matching this with the data available?
Let Data Technology show you how we can help you transform your business using AI and Automated Machine Learning
0agree that AI will be integrated into all enterprise applications within three years
AI and Banking
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.
Data Technology can help you use AI to achieve these goals by leveraging your own data about clients, how their needs have evolved, and their channel preferences.
See how Data Technology can help you
- Determine: which client is likely to need which product or service
- Deepen relationships: with customers
- 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 preference
- 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
- Forecast losses: more accurately
- Reduce middle and back: office cost from process failures and error correction
- 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.
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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.
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.
Get models to production faster using DataRobot’s low-risk model deployment options, including code generation, deployment to Spark, and API-based deployment capability.
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.
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.
AI Success Stories
- 01 / 04
“Our AI platform allows us to build and deploy highly accurate credit default models in a fraction of the time it takes using traditional data science methods”
– Director of Mortgage Innovation
- 02 / 04
“We can now understand the data being fed into our models without blindly feeding everything we get into our system. The AI platform makes my fraud prevention team very effective”
– Head of Data Insights
- 03 / 04
“We recognise that data is at the core of our business strategy, to deliver an exceptional personalised customer experience. The AI platform transforms the economics of extracting value from this resource”
– VP of Strategy Analytics
- 04 / 04
“Our AI platform justifies its place by providing value and returning significant ROI for asset managers immediately”
– Head of Enterprise Analytics