Top 5 Decision Management Posts of 2022: AI and Digital Jane

From diversity in AI to the film Highlander to transformative banking experiences, these decision management topics pointed the way to the future

The promise of AI and FICO Platform dominated the top posts of 2022 in the Decision Management category. Along with these discussions, we introduced a character we call Digital Jane. Here are the top 5 posts from 2022.

1. Women in AI: Diversity Can Lead to Better Models

Louise Lunn on Women in AI

 

On the United Nations International Day of Women and Girls in Science, FICO’s Louise Lunn discussed one of the real challenges in AI, fighting the bias that can be coded into the models themselves.

All AI models are trained on datasets, and these datasets frequently have coded into them a level of bias. It’s up to the data scientists to correct for this, and that is why it is so important to achieve more diverse teams building AI.  

Recognising that we need diversity in innovation and teams is the first step. In many cases, AI learns from data generated by human actions. Left unchecked by data scientists, algorithms can mimic our biases, conscious or not. However, we can mitigate those biases by including people across race, gender, sexual orientation, age, and economic conditions to challenge our own thinking views. By bringing in people with different thoughts and approaches to our own, analytics teams will see a quick improvement in their code.

Big Opportunities for Women in AI

For any girl or woman thinking about data science as a career route, the opportunities are immense. Data scientists are a new breed of analytical experts, responsible for collecting, analysing, and interpreting extremely large amounts of data. These roles are an offshoot of several traditional technical roles, including business domain expertise, mathematicians, scientists, statisticians, and computer professionals.  All these different jobs fit into the disciplines of a data scientist.

The insights that data scientists uncover should be used to drive business decisions and take actions intended to achieve business goals. While executives are generally smart individuals, they may not be well-versed in all the tools, techniques, and algorithms available to a data scientist (e.g., statistical analysis, machine learning, artificial intelligence, and so on). Part of the data scientist’s role is to translate business needs into algorithms.

“I would argue that if you accomplish diversity in your teams, you’ll make better AI because your teams will be better at spotting bias and correcting for it,” Louise said. “Different backgrounds drive more creative thinking, and more diverse teams tend to improve a company’s ability to solve problems. That’s just as true in data science as it is in other fields.”

A version of this article appeared in WeAreTechWomen.

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2. “There Can Be Only One”: The Highlander Principle of Data Science

FICO Chief Analytics Officer Scott Zoldi cited the cult film Highlander and its quote “In the end, there can be only one” in noting that “There can be only one” has become a bit of a mantra in the tech world. “As the head of FICO’s data science organization, it has been a mantra of mine for a long time, too,” Scott said. “It’s been a fixture in my talks about model development for years, and in June 2021 I wrote about the Highlander Principle in a blog about the importance of Auditable AI.”

Highlander photo
"There can be only one!" as Sean Connery and Christopher Lambert know.

Scott posed the following questions an organization needs to ask in developing Auditable AI:

  1. How is the analytic organization structured today? 
  2. How is the existing governance committee of analytic leaders structured? 
  3. How is Responsible AI being addressed? 
  4. What is the state of the data ethics program and data usage policies?
  5. What are the AI development standards? 
  6. How is the company achieving Ethical AI? 
  7. What is the company’s philosophy around AI research? 
  8. Is the company uniformly ethical with its AI? 

(The questions above were edited for brevity. For the full version read “Beyond Responsible AI: 8 Steps to Auditable Artificial Intelligence.”)

Why “There Can Be Only One”

Many organizations, including data science teams, derail innovation with internal competition that fragments resources and energy. In a recent article about how to foster healthy intracompany rivalry, MIT Sloan Management Review has this as its first guiding principle:

1. Unify with common purpose. To engage in healthy competition inside organizations, people need to see themselves as united by a common purpose and a higher calling. At NASA, for example, employees’ strong belief that their work contributes to a greater purpose provides an effective counterbalance to a results-driven and competitive internal culture. Every year for nearly a decade, NASA has ranked No. 1 in employee satisfaction among large federal agencies.

For data science organizations, the Highlander Principle establishes not only a single common purpose — to create Responsible AI that is innovative, ethical, explainable and auditable — but a single detailed corporate-wide framework on how to do so. This is what AI governance and model governance are all about: hammering out a singular corporate vision for fair, unbiased use of AI, and governing the path to achieve it with common principles, processes and tools.

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3. Environmental Solutions Using Analytics

At FICO World 2022, Bill Waid gave a mainstage presentation on how analytics ant FICO Platform can make a difference to a business’s ESG (environment, social and governance) goals. He cited a real-life example of this from Bradesco, a FICO customer and one of the largest private banks in Brazil.

“Our purpose is to contribute to the sustainable development of Brazil and to support all Brazilians in achieving their goals, offering diversified and accessible financial and insurance solutions.

We are committed to increasingly improving the management of social and environmental factors in our businesses and in supporting the market in the same direction, helping our customers in the transition to a less carbon-intensive economy and more resilient to the climate change impacts.”

So, the question is, how can FICO platform and FICO’s support help organizations make green decisions?  

Bill Waid at FICO World


One of the ways that this happens is in our optimization space. We've been a leader in this space for well over 20 years and we have many organizations that use optimization in areas like supply chain management. This helps them to reduce carbon footprint by finding optimal schedules and ways of increasing efficiency.

Here are just a few examples of the work we do with clients to help them make greener decisions.

Mexico’s Traxión Will Save 1,580 Metric Tons of Emissions and USD2.5M in Costs

Traxión, the leading mobility and logistics company in Mexico, has used FICO route optimization technology to help it use fewer vehicles, save on fuel costs, cut emissions and reduce fleet wear and tear. The company has already saved 2.9 million kilometers in travel, USD$725,000 in costs and 458 metric tons of emissions after implementing just 11% of the optimal solution. Traxión is on track to reduce empty trips by 20 percent which will save it over 10 million kilometers, USD$2.5M in costs and an impressive 1,580 metric tons of emissions once its operations are fully optimized. (Read the full story here)

Denmark’s Ørsted finds innovative solution for design of cable layouts for offshore wind farms

Ørsted, the world's leading offshore wind farm developer, has used FICO® Xpress Optimization to develop a novel digital solution for designing an important part of their wind farms. This has enabled the Danish company, which has 30 wind farms in operation or under construction, to achieve significant savings while reducing overall design time and improving its ability to investigate different scenarios. This allows Ørsted to roll out wind farms faster than they could before. (Read the full story here)

Europe’s Hoist finance saves hundreds of tons of carbon through digital collections

Hoist Finance, a consumer debt purchaser in Europe, manages 18 million accounts across 10 countries, and before working with FICO customer decisions were managed using 14 different systems. Hoist sought to unify decisioning technology across all regions, deploy new strategies faster, and improve results through testing and simulation. By bringing that together and digitizing that they were able to save hundreds of tons of carbon for every 10% increase in digital collections. (Read the full story here)

An environmentally focused organization is also important to the next wave of customers. Today’s youth will not bank with any organization that cannot handle all their needs digitally. “My oldest daughter actually switched her bank five times until she found the right one that can actually service her,” Bill said. “She also cares as all millennials do, what the commitment of the organization they bank with is to the environment and they will not bank with them unless there is a firm commitment and action plan in place. Our kids are the future of our business, they are our future customers.”

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4. Four AI Predictions for 2023: From the Great Correction to Practical AI

As one of the pioneers in the AI field, Scott Zoldi has unique perspectives on what’s happening in the industry. In his annual predictions post, he wrote that the AI field is experiencing a Great Correction from moonshot projects to normalcy, nowhere more evident than in more realistic approaches to artificial intelligence and its attendant machine learning (ML) models, algorithms and neural networks. “I’m calling this new pragmatism Practical AI, and I predict this technology will rise in 2023 like a phoenix from the ashes of years of irrational exuberance around artificial intelligence,” Scott wrote.

Scott Zoldi on Practical AI

 

He offered these four predictions:

1. Novelty applications will be out, practical applications will be in

Generative AI has been a big buzzword lately, with slick image generation capabilities grabbing headlines. But the reality is, Generative AI isn’t a new technology; my data science organization at FICO has been using it for several years in a practical way to generate synthetic data, and to do scenario testing as part of a robust AI model development process.

Here's an example of why we need to focus more on practical uses of Generative AI: Open banking represents a huge revolution in credit evaluation, particularly for the underserved. However, as this new financial channel takes off, collecting a corpus of data to build real-time, customer-aware analytics is lacking. Generative AI can be applied practically to produce realistic, relevant transaction data for developing real-time credit risk decisioning models. This could greatly benefit buy now pay later (BNPL) lenders, which are now exposed to high default rates due to inadequate analytics, jeopardizing open banking’s potential to better serve the underbanked in credit evaluation.

2. Artificial intelligence and machine learning development processes will become productionalized

To achieve production-quality artificial intelligence, the development processes themselves will need to be stable, reliable and productionalized. This comes back to model development governance, frameworks for which will increasingly be provided and facilitated by new artificial intelligence and machine learning platforms now entering the market. These platforms will set standards, provide tools and define application programming interfaces (APIs) of properly productionalized analytic models, and deliver built-in capabilities to monitor and support them.

AI governance is a major focus of my work, and I predict that in 2023 we will see artificial intelligence platforms and tools increasingly become the norm for facilitating in-house Responsible AI development and deployments, providing the necessary standards and monitoring.

3. Proper model package definition will improve the operational benefits of AI

Productionalizing AI includes directly codifying, during the model creation process, how and what to monitor in the model once it’s deployed. Setting an expectation that no model is properly built until the complete monitoring process is specified will produce many downstream benefits, not the least of which is smoother artificial intelligence operations:

  • AI platforms will consume these enhanced model packages and reduce model management struggles. We will see improvement in model monitoring, bias detection, interpretability and real-time model issue reporting.
  • Interpretability provided by these model packages will yield machine learning models that are transparent and defensible.
  • Rank distillation methods will ensure that model score distribution and behavior detection are similar from model update to model update. This will allow updates to be integrated more smoothly into the existing rules and strategies of the larger artificial intelligence system.

4. There will be a handful of enterprise-class AI cloud services

Clearly, not every company that wants to safely deploy AI has the resources to do so. The software and tools required can simply be too complex or too costly to pull together in piece-parts. As a result, only about a quarter of companies have AI systems in widespread production. To solve this challenge and address a gigantic market opportunity, I predict that 2023 will see the emergence of a handful of enterprise-class AI cloud services.

Just as Amazon, Google and Microsoft Azure are the “Big Three” of cloud computing services, a few top AI cloud service providers will emerge to offer end-to-end AI and machine learning development, deployment, and monitoring capabilities. Readily accessible via API connectivity, these professional AI software offerings will allow companies to develop, execute and monitor their models and algorithms, while also demonstrating proper AI governance. These same cloud AI platforms could also recommend when to drop down to a simpler model (Humble AI) to maintain trust in decision integrity.

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5. Meet Digital Jane, She’s Your Customer and She Has High Expectations

Meet Jane. Jane is 28-year-old woman sharing an apartment in the big city with friends. She uses her mobile phone to conduct a lot of her day-to-day life, including connecting with friends, making purchases, checking her bank account, and even buying a new car. She wants a bank that can keep up with, and even anticipate, her needs. Jane is not alone in that expectation. In a fast-paced, digital world, financial institutions who succeed put customers at the center of their business.

How? Enter FICO. FICO Platform is designed to predict, analyze, and optimize customer interactions in real-time to help organizations make better customer-level decisions. The series of short videos below demonstrates how financial institutions - using analytics, decision modeling and AI - can seamlessly elevate Jane’s financial interactions in a typical day in order to strengthen customer loyalty.

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How FICO Can Help You Win with AI and Decision Management

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