Artificial intelligence is not new for the financial industry and many companies are already leveraging artificial intelligence in sales, compliance, risk, customer experience or internal operations.
Data is at the core of the business model of insurance and banking and it is obvious that top executives should be looking:
- For ways to improve its analysis
- For new sources of data that could be transformed into new insights and new streams of revenues by the new generation of machine-learning algorithms
- For ways to find advanced and diversified usages for artificial intelligence besides the mainstream use cases
The challenges ahead for top executives in banking and insurance are numerous, with an ongoing crisis on top of profound changes in the industry. In this context, artificial intelligence is an opportunity and executives should tap more into the amount of data their company generate to find answers and solutions to these big challenges they are facing. There is a real urgency to succeed in the deployment of artificial intelligence solutions that produce results.
For the banks and insurance companies that have been in a journey to deploy artificial intelligence for already a couple of years, adapting their strategies and initiatives to take into account the development of responsible AI may be a new challenge for them. At the same time, while 85% of the artificial intelligence models deployed do not produce the expected results due to bias in the data, algorithms or teams managing them, this is also a solution.
With such a gap between expectations and realities, we need to change the way we approach the development and deployment of artificial intelligence use cases. This is where responsible artificial intelligence comes into the game.
Let’s stop for a minute to define what we mean with responsible artificial intelligence.
Responsible artificial intelligence encompasses different concepts, also found under the label ‘ethical AI’ or ‘trustworthy AI’, and is now at the core of the European Union strategy on artificial intelligence (it is also paramount for other international organizations such as the OECD and UNESCO), which resulted in the announcement of a new regulatory framework on the 21st of April 2021. It is so important in the countries that favour the development of responsible AI that several regulators themselves, such as the French prudential and control authority, recently released guidelines on how banks and insurance companies should use responsible AI.
What makes responsible AI different is the fact that companies involved in the development of responsible artificial intelligence prioritize technical and organizational solutions that promote well being, privacy, robustness, the capacity to explain in an intelligible way the outcomes produced by algorithms, the absence of avoidable discriminatory biases or the capacity to maintain AI on a long term and to manage its lifecycle.
The operational benefits of deploying responsible AI for a company are numerous :
- Responsible Artificial intelligence models improves the economic results of an existing AI strategy as the number of erroneous outcomes of deployed models is reduced
- Responsible Artificial intelligence models have a reduced number of bias compared to traditional AI models. They also treat more fairly different customers without sacrificing economic performance
- Responsible Artificial Intelligence models produce decisions and their explanations, which help the frontline teams to create compelling stories to their customers, to helps data-scientists deploy the most appropriate models in production and validation and control teams to quickly audit the models as they produce decisions
- Responsible artificial intelligence models are more robust and can be maintain over time
- Responsible artificial intelligence ensures a good governance that generates trusts and accelerates adoption across the company
Therefore adopting responsible artificial intelligence practices right now ensures that you will be compliant as more and more regulators will edict their guidelines. Some have already been assessing these benefits and produced their first set of recommendations in advance of the European regulation. Companies adopting best practices of responsible artificial intelligence generate significantly more results than the other ones.
That’s why as a company aiming to become a front runner, the time to act is now !
How DreamQuark can help you to leverage responsible AI
Brain, our trusted AI software is built from the ground up around the principles of responsible AI to let your team build and maintain explainable and bias free artificial intelligence models that produce real results.
With Brain, your business analysts and data-scientists, are guided through a simplified user journey that democratizes the access to artificial intelligence.
Through Brain interfaces they are warned about potential biases that can affect the economic performance and robustness of your artificial intelligence initiatives before and after their deployment.
Brain helps your team to deploy the most accurate and business efficient models (for example, if a model will be used to power a churn reduction campaign, you will know in minutes how many customers should be contacted, how many of these customers may indeed churn, how much it will cost you to reach them and how you’ll save by retaining a portion of these customers).
The AI models generated with Brain come with their explanations by default. Not only will your team know which factor are important for the models they aim to deploy, but your sales, compliance or risk teams will be able to explain to their customers why they have made a specific proposal, denied a credit or a suspicious transaction.
As we know that only a model that is deployed generates results, we help you deploy these models in one click and provide you with metrics to analyze their usage over time.
Once deployed, we monitor the model for you. Because data is changing over time, so does the performance of a model and you need to be warned when a model needs to be retrained. With Brain data drift and stability analysis tools you can continuously monitor the model you have deployed in production to quickly update the models that are likely to produce erroneous results before they negatively affects your revenues and profits.
Moreover with Brain decision monitor, your team knows when a model recommendation is based on previously unseen data, if data was missing when the score has been produced or if the data has significantly changed from the data used to generate the initial model. These tools enforce trust and control in the decisions that are generated and help increase the accountability of your frontline team.
Finally, as artificial intelligence only produces value if it is used, Brain doesn’t come single. We also provide front tools for advisors who can get their next best actions in a friendly and contextualized advisor cockpit.
Responsible AI is key as companies, regulators, employees and clients are asking for more business ethics in a post-covid era and AI is increasingly under scrutiny. Moreover, with 83% of executives believing that AI need to be explainable to be adopted, responsible AI is the driver of success for your AI initiatives. With Brain it has never been so easy to leverage responsible AI at scale to generate actual business results with AI.
Discover how you can leverage responsible AI, and contact us today!