Monthly Archives: March 2022
The ecosystem of credit lending platforms and technologies has rapidly grown in the past year. Lenders now find themselves in an increasingly competitive market with new players emerging on the scene. More companies now have access to advanced analytics and automation capabilities, and this is helping businesses improve the accuracy and inclusivity of consumer lending decisions – a giant step toward achieving their growth ambitions. Our recent research shows that one of the top priorities for businesses has been to invest in new artificial intelligence and machine learning models for smarter customer decisions. But how effective is building new AI models without considering the data? What is data-centric AI? Building AI models on fixed data has already become an outdated approach. But by coupling data with the best model, better outcomes can be achieved. The concept of data-centric AI was coined by leading thinker in the AI space, Andrew Ng. Ng believed that models in production are only as good as the point-in-time data used to build them. As businesses continue to receive new data, this data needs to feed back into the model if it’s going to continue delivering the best results. This continuous loop of enriching the model with new data can be applied across use cases. The value of data-centric AI models for acquiring new customers By using the latest available data, rather than from 6-12 months ago or longer when the model was originally developed, data-centric AI models can: • More rapidly account for changes in the economy and consumer finances • Reach under-represented populations and provide greater access to credit • Take advantage of newly available types of information from data providers The value of data-centric AI in existing frameworks More observations AI is often limited by the data that was used to create the model. By using a more fluid open-source alternative, different data sets can be inputted to get more observations based on different characteristics and findings. For example, if a business wants to acquire a new type of customer, traditional AI would require a new model with new data sets to be in order to target this new customer. With data-centric AI, businesses can use an existing model and simply expand the data, thus allowing the model to work far more efficiently and target a new consumer base. It is a shared view that businesses should not build models with just their own data, because those data sources are too limited. At the very least, businesses want to combine data with a peer sample. However, an even better way is to use hybrid data sets in order to get the most observations. Data-centric AI makes that process easy without the need to create different models to see different outcomes. Up-to-date data The world is in a state of flux—populations change, people change. This means that the data pools AI models draw on may be compromised, no longer relevant, or have new meaning over time. It’s important to keep AI data sets recent and up to date, and not assume that the models used two years ago still apply today. For AI models to operate efficiently they need current, relevant data. Having a data-centric approach and sweeping through collected observations is essential for any business relying on their AI solutions. Businesses must have processes to understand and test their data to be sure the values are still adding up to what they should be. Being disciplined about data hygiene, all the way back to the source, is a necessity. Enriched and expanded data With model-centric AI, businesses are limited by the data they start with. Data-centric AI makes it possible to expand on the current customer base, which already includes data on customer attributes, with new potential customers that might mimic characteristics of a business's current base. Expanded data can also play a role with financial inclusion and credit worthiness. Having a low credit score does not necessarily mean the consumer is a bad risk or that they shouldn't be allowed access to credit—sometimes, it could mean there is simply a lack of data. Expanding data to include varied sources and adding it to current models without changing their structure, enables businesses to provide credit for individuals who may not have originally been accepted. This new approach in AI is creating solutions that are far more inclusive than previously possible. Data has massively expanded and is constantly evolving. By using data combined with advanced analytics, such as AI, there will be more sophistication in the observations that come from the data. This will allow businesses to better decide what data they choose to rely on while ensuring accuracy. By using expanded data sources, the outcomes of models are changed, leading to more inclusive models better fit for decision making and improving performance. "Models in production are only as good as the point-in-time data used to build them." Andrew Ng Infographic: Why data-centric AI leads to more accurate and inclusive decisions Stay in the know with our latest research and insights:
Global fraud predictions for 2022, plus a closer look at regional fraud trends. Stay in the know with our latest research and insights:
During the week of International Womens' Day, we shine a spotlight on the women thought leaders across Global Decision Analytics. In this Every Woman Forum, Tech Hub article, Angela Beteta Quesada, Principal DevOps Engineer, Global Decision Analytics, discusses the importance of relatable role models for women in the tech industry and how making the right choices in where and how you work can open up a wealth of different people to learn from. "As a role model I think I am relatable; I like to take risks and opportunities that are a challenge to me — and that I think are going to be good fun. That is how my whole career has developed. I also work well with people and have good communication skills — when I was a junior Java engineer, my mantra was ‘leave it with me’. I think my managers saw that they could throw things at me and have confidence that there was going to be a good result, which is a mindset I still have." Read the article to find out: How important were relatable role models at the start of your tech career? What makes an effective role model, and why? What do you feel young women at the beginning of their career can see in you that will be useful to take with them in their career? How important is it to see more role models combining the realities of motherhood and a high-pressure career? What advice would you give to a young woman starting her career in tech? Do you still look for role models in your own career? Stay in the know with our latest research and insights:
During the week of International Womens' Day, we shine a spotlight on the women thought leaders across Global Decision Analytics. In this Juniper Research interview, Kathleen Maley, VP of Analytics Product Management talks about the current state of data analytics, with the backdrop of Juniper Research's Future of Digital Awards and its recognition of AIS. Watch the video to discover: Current problems with data analytics Broad nature of activities of what is now defined as analytics Model development, model scoring, model regulatory control, model risk management and model deployment Where is data coming from - is it clean and do we understand it? Importance of humans in the development of algorithms Lack of data - where do we need to close gaps? How does looking at the past help with looking to the future - the importance of current/real-time data The expense of maintenance - tech stack - there are now alternatives Democratization of data - expanding credit access by using non-traditional sources of data Talent shortage of data scientists - low-code and no-code Extracting data value for businesses when data is ever-expanding Stay in the know with our latest research and insights:
Did you miss these February business headlines? We’ve compiled the top global news stories that you need to stay in-the-know on the latest hot topics and insights from our experts. Credit card fraud jumps to a five-year high The Mail Online covers why, according to Experian, cold-calling, text scams and crypto fraud will rise in 2022, while also pointing to a rise in fraudulent credit card applications, particularly during the busy Christmas period. The future of fraud: How digital dependency creates infinite opportunity for scammers In this Forbes article, David Armano writes about the futuristic nature of Experian's Future of Fraud Forecast for 2022, and why digital transformation has created new opportunities across the fraud landscape. Your digital impression is your first impression, here's how to make it count Inc.com looks at why your digital first impression is the new first impression, and what you can do about it. Read about the four areas to focus on to put your best foot forward digitally in 2022. The markets, Ukraine, cyberthreats, and supply risks In this episode of The Tape podcast, David Britton, Global Strategy for Identity & Fraud at Experian, talks about internet fraud and how it affects consumers and businesses. Fraud suspicions have a record high in 2021 in Brazil, says Serasa Last year Brazil had 4.1 million transactions suspected of fraud, according to the Serasa Experian Fraud Index. Read about what type of fraud threats are included, and what 2022 might look like across the fraud landscape. Stay in the know with our latest research and insights:
