Can AI transform the Financial Services industry?

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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.

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Challenge 1

Solution 1

Client Experience

Financial Market firms must work to digitize and transform for growth and operational efficiencies. By aggressively innovating and differentiating, they can secure a larger share of assets and create a next generation client experience.

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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

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Challenge 2

Solution 2

Lending

AI helps firms to reimagine their lending operations. By accelerating initiatives, leveraging their own data, and delivering bottom-line results, companies can use machine learning to improve lending models and win.

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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

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Challenge 3

Solution 3

Financial Markets

There are thousands of applications for automated machine learning to help ensure that financial market research and investment firms are empowered to make faster and better decisions.

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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

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All use cases underpin increasing revenue, reducing costs and delivering excellent customer service

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.

Credit

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.

Financial Crime

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.

Client Experience

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.

Marketing

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.

Cash Management

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.

Global Markets

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.

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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.

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