Technological advancements, such as those resulting from the adoption of artificial intelligence (AI) and machine learning (ML), are providing enterprises with more effective tools to discover insights from ever-expanding and complex data.

However, the solutions do not lie solely in technology. Financial services providers must leverage strategic thinking, embrace collaboration, and be committed to responsible innovation.

Consider the issue of financial inclusion. An estimated 1.5 billion people worldwide are still unbanked, leaving them vulnerable to economic shocks and limiting their opportunities for growth.

Expanding financial inclusion is not only a moral imperative but also a significant economic opportunity.

Inclusive financial systems foster economic growth, reduce inequality, and create more resilient communities.

Using alternative data for financial inclusion

This is where AI and alternative data come into play. Traditional credit risk models rely heavily on established data sources, such as credit bureau scores.

While these models have served us well, they exclude individuals and businesses without formal credit histories.

Alternative data, such as online behaviour, utility and rent payments, and even mobile phone usage, can provide a measure of creditworthiness for the unbanked and underbanked.

By integrating this data with advanced ML techniques, we can build better models and provide an increased level of access to credit for many unable to do so in the past.

AI and ML are better at processing and analysing vast and complex datasets such as digital footprints and transactional history.

The more complex ML algorithms can uncover patterns in these datasets that traditional methods might miss.

However, not all alternative data is complex. For example, a consumer who regularly pays rent or utility bills on time is creditworthy but is not able to demonstrate this to lenders today.

Reporting such data to the credit bureaus, and incorporating them into risk models and decisions, can expand access to credit for underserved populations while maintaining the underlying integrity of risk assessments.

Of course, data quality and governance remain critical to ensuring that the insights derived are both accurate and fair. In particular, online behaviour data, such as what you like and follow on social media, can be highly correlated to race, religion, nationality, and other prohibited factors.

As such, building models on this data requires human oversight as well as clear ethical and legal guidelines to avoid unintended biases that could perpetuate existing inequalities.

Ethical AI practices, supported by human oversight, are essential for building trust with consumers and regulators alike.

Addressing housing inequalities

Housing disparities, fuelled by lending inequities, highlight how AI and alternative data can be used to overcome systemic issues.

Given the history of South Africa, many communities faced discriminatory practices where access to loans and credit was denied or limited based on racial demographics.

This perpetuated economic inequities, limiting home ownership opportunities and generational wealth creation for millions.

By analysing vast datasets using AI, financial institutions can identify patterns of inequity and take corrective action.

For instance, a study conducted by the Center for NYC Neighborhoods in partnership with SAS revealed clear disparities in housing outcomes across different neighbourhoods in New York City.

Leveraging AI-powered analysis, the study demonstrated how alternative data could pinpoint areas of systemic inequity and inform strategies to address them.

When used responsibly, AI and alternative data can help create a more equitable housing market, fostering long-term economic resilience and inclusivity.

Collaboration and innovation

The future of financial services, in a world of vast complex alternative data and powerful AI algorithms, will be shaped by our ability to harness technology responsibly and collaboratively.

AI and alternative data offer immense potential, but their success depends on collective efforts involving regulators, technology providers, and industry peers.

For example, reporting utilities and rent payments to the credit bureaus securely can enable better credit risk assessments within an already trusted framework.

Existing regulatory, governance, and ethical practices at lenders must be adapted to new and challenging types of data as well as algorithms that are not as explainable or transparent as previous ones.

Trust is the cornerstone of any financial system. Consumers must trust that lenders are not only sourcing their personal data ethically but also using them in models with a high degree of oversight that results in fair and unbiased decisions.

Fundamentally, the goal is clear. We must collaborate to create a financial ecosystem that is resilient, inclusive, and sustainable.

By leveraging the power of AI and alternative data, we can not only address the challenges of today but also build a foundation for a more equitable future.

This strategic acquisition aims to enhance SAS’ robust data and AI portfolio, further equipping its customers with critical and timely synthetic data generation capabilities as their use of AI rapidly expands.

“Our acquisition of Hazy’s IP represents a pivotal step in our commitment to innovation in the next generation of data management and AI,” said Jim Goodnight, CEO of SAS. “Hazy is a pioneer in bringing synthetic data to market as a viable enterprise product, and analysts rank it among the top software providers in its category. By integrating their technology, we can offer our customers unparalleled opportunities to harness data safely and effectively, enabling them to experiment and model scenarios that were previously out of reach and gain a competitive advantage.”

This move positions SAS at the forefront of data innovation, enabling more robust and secure AI applications, with future integration opportunities with SAS Viya.

By integrating Hazy’s synthetic data capabilities, SAS will empower customers to innovate and conduct deep research, overcoming challenges related to data availability, access or quality.

Leading the charge in synthetic data innovation 

According to Kathy Lange, Research Director, AI Software at IDC,

“Synthetic data is a game-changer for companies implementing AI solutions, especially in sectors with strict privacy regulations like healthcare and finance. SAS’ acquisition highlights the growing requirement for synthetic data as an integral component of a modern AI toolkit, addressing data scarcity and privacy issues, and improving model accuracy while reducing biases.”

Synthetic data, which mirrors the statistical patterns of real data without exposing private, identifiable or restricted information, mitigates risks associated with real data and enhances the scope of data available for analytics and AI.

This augmentation leads to robust, reliable results and innovative solutions for data scientists who can shape and balance data sets more effectively.

Bryan Harris, chief technology officer of SAS, added,

“Analysts predict that by 2026, 75% of businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023. For SAS customers, this marks a strategic leap, solidifying SAS’ leadership in AI and analytics. With synthetic data, customers can innovate and research more deeply, accessing data that was previously out of reach due to availability, access or quality issues.”

Integrating Hazy’s technology expands upon SAS’ initial announcement of SAS Data Maker in early 2024. SAS Data Maker addresses data challenges by generating synthetic data that statistically represents original data sets without compromising privacy, while simplifying processes and saving resources.

With Hazy technology integrated into SAS Data Maker, the ability to simulate multiple future scenarios will give SAS customers a significant competitive advantage through:

• Enhanced innovation and research by providing access to rich, synthetic data sets that were previously out of reach, fostering new opportunities and breakthroughs.
• Faster time to market via rapid generation of high-quality synthetic data, accelerating the development cycle for AI projects.
• Trustworthy AI systems with robust synthetic data processes and diverse synthetic data sets, enabling organisations to develop reliable AI systems that adhere to ethical standards.
• Increased data privacy and security by generating synthetic data that does not expose real, identifiable information, allowing organisations to operate with confidence.
• Cost savings by minimising the reliance on costly data collection methods, making data for analytics more accessible.

The enhanced data solutions resulting from this acquisition will be available globally, with an initial preview expected in early 2025.

A donation of SAS software, computing capacity and training, with a combined value of US$10 million, includes AI capabilities and content that will not only help higher education students learn how to use AI but also how to do so responsibly.

The project is an initiative of the Commonwealth Secretariat’s Commonwealth AI Consortium (CAIC), which aims to build technological capacity with an emphasis on small states and young people.

Higher education students and educators in Commonwealth countries will have free access to SAS Viya for Learners.

SAS Viya is a comprehensive, cloud-native platform that provides robust capabilities for data analytics, machine learning and AI. It is used by thousands of organisations in industries like banking, education, government, healthcare, insurance and retail.

Students and educators will also have access to the digital learning environments SAS Skill Builder for Students and the SAS Educator Portal.

These platforms offer digital courses, certification programs, and hands-on learning opportunities designed to build and validate analytics and AI skills.

Additionally, the Educator Portal includes teaching materials that make it easy to integrate analytics content into curricula.

The Commonwealth’s postsecondary institutions are also invited to participate in the Curiosity Cup – SAS’s global student data competition – and the SAS Hackathon.

These free competitions provide an excellent opportunity for students to apply their analytical skills to real-world problems, collaborate with peers and gain recognition on a global stage.

Suresh Yadav, Senior Director of AI, Trade, Oceans and Natural Resources at the Commonwealth Secretariat said:

“In our rapidly evolving digital landscape, we face not just a digital divide, but a critical infrastructure divide. If we fail to address this gap and make essential resources available to our youth, we risk losing an entire generation to technological inequality. This isn’t just about access to information; it’s about empowering young minds with the tools to innovate, create, and solve global challenges. If we do this right, we’re not just connecting students to technology – we’re connecting them to limitless possibilities. Our partnership with SAS is a vital step towards ensuring that no young person in the Commonwealth is left behind in the digital revolution.”

There are 56 countries in the Commonwealth, including 33 small states. The SAS collaboration will initially focus on the Caribbean before expanding to other countries, with the goal of training 10,000 students and educators over five years.

This supports the Commonwealth Secretariat’s priorities of promoting digital transformation and sustainable development and building a diverse global workforce capable of supporting AI-driven transformation.

For over 40 years, SAS has partnered with academic institutions, government organisations and corporations alike to unleash the power of data to improve decisions and transform organisations.

SAS is used at more than 3,000 colleges and universities around the world, and through the company’s Global Academic Programs, it has university partnerships in Commonwealth countries in every region.

According to Statista, the AI market exceeded $184 billion in 2024 and is projected to race past $826 billion by 2030.

“We have spent decades equipping educators with resources and students with the skills they need to seize the hottest jobs in the tech market,” said Sean O’Brien, SAS Senior Vice President, Education. “Those jobs are now in AI. We are excited to help students change their lives and grow the tech sectors in Commonwealth countries.”

Learning to use AI responsibly

The rise of powerful AI technologies presents incredible opportunities but has also stoked fears, and regulations are emerging around the world to address the ethical use of AI. SAS’ commitment to responsible innovation is embedded in its culture and technology.

Commonwealth students and educators will be able to use SAS Viya’s built-in trustworthy AI capabilities like bias detection, explainability, decision auditability, model monitoring, governance and accountability.

Reggie Townsend, SAS vice president of Data Ethics, spoke at the Commonwealth launch event.

“This collaboration will bring AI capacity to populations that haven’t always benefited from the technological advances that power the world’s largest economies,” said Townsend. “These students will not only gain AI skills coveted by employers around the world, but they will also learn how to wield these powerful technologies ethically in ways that benefit society.”

It’s hardly any wonder then that 83% of anti-fraud professionals anticipate adding the technology to their anti-fraud armaments within the next two years, according to the latest anti-fraud tech study by the Association of Certified Fraud Examiners (ACFE) and SAS.

The 2024 Anti-Fraud Technology Benchmarking Report is the third instalment of a global research study initiated by the ACFE and SAS in 2019.

This latest edition reflects insights from nearly 1,200 ACFE members surveyed in late 2023. The survey data reveals key trends in the evolution of fraud fighting since 2019. Among them:

• Interest in artificial intelligence (AI) and machine learning (ML) technology is higher than ever. 18% of anti-fraud pros currently count AI/ML among their fraud-fighting tools. Another 32% anticipate implementing these technologies in the next two years – a peak since the study’s inception. At this rate, use of AI/ML in anti-fraud programs will almost triple by the end of next year.
• However, AI and ML adoption consistently lags expectations. Despite fervent interest, adoption of AI and ML for fraud detectionand prevention has grown only 5% since 2019. That figure falls far short of the anticipated adoption rates revealed in the 2019 and 2022 studies (25% and 26%, respectively).
• While the use of many data analysis techniques has plateaued, the application of biometrics and robotics in anti-fraud programs has risen steadily. Use of physical biometrics has climbed 14% since 2019, now cited by 40% of respondents. 20% of survey respondents reported using robotics, including robotic process automation (RPA), up from 9% in 2019. The use of these technologies is notably highest in banking and financial services, with 51% using physical biometrics and 33% using robotics.

“The accessibility of GenAI-powered tools makes them incredibly dangerous in the wrong hands,” said John Gill, ACFE’s president. “Three in five organisations foresee increasing their anti-fraud technology budgets over the next two years. How they invest these funds will determine who will seize the upper hand in what’s become a technology arms race with criminal enterprises. It’s an uphill battle when you consider that, unlike the fraudsters, organisations face the added challenge of having to use these technologies ethically.”

“Explosive interest in advanced analytics techniques juxtaposed with much more modest adoption rates proves the complexities of scaling the AI and analytics life cycle,” said Stu Bradley, senior vice president of Risk, Fraud and Compliance Solutions at SAS. “It also underscores the importance of choosing the right technology partner. AI and machine learning aren’t simple, plug-and-play applications. However, their benefits can be more readily realised by deploying modularised solutions across the risk management spectrum on a single, AI-powered platform. That’s SAS’ approach with cloud-native, language-agnostic SAS Viya.”

Explore trends by industry, geography and more

Complementing the benchmarking report, SAS’ online data dashboard allows users to analyse survey data by industry, geographic region and company size.

Survey respondents work in 23 industries – most prevalently banking/financial services and government/public administration (each accounting for 22% of respondents), but also professional services (13%), insurance (5%), health care (4%), manufacturing (4%), technology (4%), education (4%) and more.

Their employer organisations span the globe and range in size from fewer than 100 employees to more than 10,000.

The data dashboard at SAS.com/fraudsurvey contains cross-industry, anti-fraud tech trends and sentiments around:

• Data analysis techniques organisations use to fight fraud
• Risk areas where organisations apply data analytics to monitor for fraud
• Data sources organisations use in their anti-fraud initiatives and their perspectives on data-sharing consortiums
• The prevalence of case management and digital forensics/e-discovery software
• Challenges organisations face in implementing new anti-fraud technology
• How GenAI is affecting organisations’ anti-fraud programs

The future of GenAI: Boom or bust?

Will the deployment of GenAI in anti-fraud programs skyrocket in line with survey respondents’ passionate intent? Or will real-world challenges like budgetary restrictions, data quality and skills gaps inhibit its predicted ascent? Only time will tell – but organisations can’t be too careful in embracing GenAI and other AI technologies. SAS believes that responsible innovation requires organisations to ask not only “could we” but also “should we?”

“The use of GenAI in anti-fraud initiatives could play a significant part in identifying anomalies, trends and indications in larger volumes of data with minimal resource concerns,” said one survey respondent. “However, the organisation will need to ensure that proper guidelines are in place to minimise errors and bias.”

“GenAI has made great strides these last few years, so it’s no surprise that organisations are incorporating it into their anti-fraud initiatives,” said Mason Wilder, ACFE’s research director. “As a society, we are still learning all the advantages and disadvantages to using the technology, but more organisations are beginning to take that first step. It will be interesting to see how quickly adoption occurs, in and out of the workplace, in addition to the technology continuing to become more advanced with time.”

“Fraud and financial crime activity are rising as is its impact in local and global markets due to an evolving economic environment and expanding technological capabilities that create new opportunities for fraudsters. In 2024 and beyond, organisations will need to be hyper-vigilant and increase their fraud-fighting efforts by deepening their investment in talent as well as advanced analytics and AI-enabled solutions that offer innovative strengths in effectively detecting, preventing and mitigating fraud. This approach ensures that they can maintain resiliency, protect consumers, and gain a competitive advantage,” says Itumeleng Nomlomo, senior business solutions manager at SAS in South Africa.

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SAS, the leader in AI and analytics, asked executives and experts across the company to predict trends and key business and technology developments in AI for 2024.

Below are some of the predictions they shared.

Generative AI will augment (not replace) a comprehensive AI strategy

“Generative AI technology does a lot of things, but it can’t do everything. In 2024, organisations will pivot from viewing generative AI as a stand-alone technology to integrating it as a complement to industry-specific AI strategies.

In banking, simulated data for stress testing and scenario analysis will help predict risks and prevent losses. In healthcare, that means the generation of individualised treatment plans.

In manufacturing, generative AI can simulate production to identify improvements in quality, reliability, maintenance, energy efficiency and yield,” says Bryan Harris, Chief Technology Officer, SAS.

AI will create jobs

“In 2023, there was a lot of worry about the jobs that AI might eliminate. The conversation in 2024 will focus instead on the jobs AI will create. An obvious example is prompt engineering, which links a model’s potential with its real-world application. AI helps workers at all skill levels and roles to be more effective and efficient. And while new AI technologies in 2024 and beyond may cause some short-term disruptions in the job market, they will spark many new jobs and new roles that will help drive economic growth,” says Udo Sglavo, Vice President of Advanced Analytics, SAS.

AI will enhance responsible marketing

“As marketers we must consciously practice responsible marketing. Facets of this are awareness of the fallibility of AI and alertness to possible bias creeping in. While AI offers the promise of enhanced marketing and advertising programs, we know that biased data and models beget biased results. In SAS Marketing, we are implementing model cards that are like an ingredient list, but for AI. Whether you create or apply AI, you are responsible for its impact. That’s why all marketers, regardless of technical know-how, can review the model cards, validate that their algorithms are effective and fair, and adjust as needed,” says Jennifer Chase, Chief Marketing Officer, SAS.

Financial firms will embrace AI amid a Dark Age of Fraud

“Even as consumers signal increased fraud vigilance, generative AI and deepfake technology are helping fraudsters hone their multitrillion-dollar craft. Phishing messages are more polished. Imitation websites look stunningly legitimate. A crook can clone a voice with a few seconds of audio using simple online tools. We are entering the Dark Age of Fraud, where banks and credit unions will scramble to make up for lost time in AI adoption – incentivised, no doubt, by regulatory shifts forcing financial firms to assume greater liability for soaring APP [authorised push payment] scams and other frauds,” says Stu Bradley, Senior Vice President of Risk, Fraud and Compliance Solutions, SAS.

Shadow AI will challenge CIOs

“CIOs have struggled with ‘shadow IT’ in the past and will now confront ‘shadow AI’ – solutions used by or developed within an organisation without official sanction or monitoring by IT. Well-intentioned employees will continue to use generative AI tools to increase productivity. And CIOs will wrestle daily with how much to embrace these generative AI tools and what guardrails should be put in place to safeguard their organisations from associated risks,” says Jay Upchurch, Chief Information Officer, SAS.

Multimodal AI and AI simulation will reach new frontiers

“The integration of text, images and audio into a single model is the next frontier of generative AI. Known as multimodal AI, it can process a diverse range of inputs simultaneously, enabling more context-aware applications for effective decision making. An example of this will be the generation of 3D objects, environments and spatial data. This will have applications in augmented reality [AR], virtual reality [VR], and the simulation of complex physical systems such as digital twins,” says Marinela Profi, AI/Generative AI Strategy Advisor, SAS.

Digital-twin adoption will accelerate

“Technologies like AI and IoT [Internet of Things] analytics drive important sectors of the economy, including manufacturing, energy and government. Workers on the factory floor and in the executive suite use these technologies to transform huge volumes of data into better, faster decisions. In 2024, the adoption of AI and IoT analytics will accelerate through broader use of digital-twin technologies, which analyse real-time sensor and operational data and create duplicates of complex systems like factories, smart cities and energy grids. With digital twins, organisations can optimise operations, improve product quality, enhance safety, increase reliability and reduce emissions,” says Jason Mann, Vice President of IoT, SAS.

Insurers will confront climate risk, aided by AI

“After decades of anticipation, climate change has transformed from speculative menace to genuine threat. Global insured losses from natural disasters surpassed $130 billion in 2022, and insurers worldwide are feeling the squeeze. US insurers, for example, are under scrutiny for raising premiums and withdrawing from hard-hit states like California and Florida, leaving tens of millions of consumers in the lurch. To survive this crisis, insurers will increasingly adopt AI to tap the potential of their immense data stores to shore up liquidity and be competitive. Beyond the gains they realise in dynamic premium pricing and risk assessment, AI will help them automate and enhance claims processing, fraud detection, customer service and more,” says Troy Haines, Senior Vice President of Risk Research and Quantitative Solutions, SAS.

AI importance will grow in government

“The workforce implications of AI will start being felt in government. Governments have a hard time attracting and retaining AI talent since experts command such high salaries, however, they will aggressively recruit for expertise to support regulatory actions. And like enterprises, governments will also increasingly turn to AI and analytics to boost productivity, automate menial tasks and mitigate that talent shortage,” says Reggie Townsend, Vice President of the SAS Data Ethics Practice.

Generative AI will bolster patient care

“To advance health and improve patient and member experiences, organisations will further develop generative AI-powered tools in 2024 for personalised medicine, such as the creation of patient-specific avatars for use in clinical trials and the generation of individualised treatment plans. Additionally, we will see the emergence of generative AI-based systems for clinical decision support, delivering real-time guidance to payers, providers and pharmaceutical organisations,” says Steve Kearney, Global Medical Director, SAS.

Deliberate AI deployment will make or break insurers

“In 2024, one of the top 100 global insurers will go out of business as a consequence of deploying generative AI too quickly. Right now, insurers are rolling out autonomous systems at breakneck speed with no tailoring to their business models. They’re hoping that using AI to crunch through claims quickly will offset the last few years of poor business results. But after 2023’s layoffs, remaining staff will be spread too thin to enact the necessary oversight to deploy AI ethically and at scale. The myth of AI as a cure-all will trigger tens of thousands of faulty business decisions that will lead to a corporate collapse, which may irreparably damage consumer and regulator trust,” says Franklin Manchester, Global Insurance Strategic Advisor, SAS.

Public health will get an AI boost from academia

“Public health is achieving technologic modernisation at an unprecedented rate. Whether overdoses or flu surveillance, using data to anticipate public health interventions is essential. Forecasting and modeling are rapidly becoming the cornerstone of public health work, but government needs help. Enter academia. We will see an increase in academic researchers carrying out AI-driven modeling and forecasting on behalf of government. It is clear after COVID-19 that the protection of our population will require exceptional technology and collaboration,” says Dr. Meghan Schaeffer, National Public Health Advisor and Epidemiologist, SAS.

Harnessing AI power responsibly

“There is tremendous market excitement around the potential business case applications for AI and generative AI, and as this potential becomes more vast and powerful as demonstrated by the forthcoming predicted trends. Yet, the rise of this technology is still not without its concerns. Data privacy, potential bias, ethics, and accuracy stand out as crucial areas requiring attention. While many companies developing and deploying AI and generative AI based solutions are simultaneously also developing guidelines for the responsible use of these technologies, establishing formal sets of standards by industry and even country or region have yet to catch up with the development, deployment and adoption – and I believe more governments will (and should) be spotlighting this more in 2024. At the heart of SAS’s approach to responsible AI innovation is the question not just of ‘could we’ but more importantly, ‘should we?’ There remains the need for human oversight in the use of generative AI, advocating for accuracy checks and the elimination of unintentional bias. And as the digital revolution beckons, we need to learn how best to co-exist with AI and leverage its benefits responsibly, ensuring the technology remains both trusted and secure,” says Itumeleng Nomlomo, Senior Business Solutions Manager at SAS in South Africa.

Want more? Visit SAS’ 2024 AI predictions page for more trends and forecasts.

Customers can use generative AI capabilities for the whole customer engagement lifecycle, from marketing planning and audience creation to journey design, creating channel specific creative, through reporting and measurement.

Unlike many other vendors, SAS’ generative AI integrations are not tied to one generative AI model provider.

SAS’ marketing customers enjoy the flexibility to choose which generative AI models they integrate and use. Customers can bring their own generative AI provider, choose models from popular AI vendors such as OpenAI, or elect to use open source, privately hosted models.

Additionally, customers can choose which capabilities to integrate and interact with, how they are trained and configured, and how they are introduced to marketing users – all via a custom integration framework.

“Generative AI is an exciting emerging capability that has naturally made its way into customer engagement marketing,” said Jonathan Moran, Head of Martech Solutions Marketing at SAS. “Using this technology in a responsible manner empowers marketers to optimise time, resources and marketing budgets. Gaining marketing and advertising efficiencies and effectiveness is what SAS Customer Intelligence 360 is all about, so it makes great sense to integrate generative AI with our solutions with responsible, trustworthy guardrails in place.”

Generative AI can help marketers create marketing plans, and identify and create additional segments and audiences to target.

It can generate suggested text for content such as email or other promotional or engagement copy.

For personalised targeting, it can provide suggestions around which demographic, psychographic, behaviour, and geographic variables to include in engagement activities.

If the marketer is not satisfied with the text or image guidelines or suggestions, the marketer is then empowered to edit this information.

Once created, approved, and activated results can be measured, reported on, and democratised throughout the organisation.

Core custom integration capabilities include:  

• Integration via the SAS connector framework to large language model (LLM) providers to brainstorm novel campaign strategies.
• Applying natural language to build audiences for targeting.
• Accelerating content generation using custom models and knowledge bases.

Generative AI as a component of responsible marketing

According to a recent Gartner research report, Predicting How Major Trends Will Shape Marketing’s Future, 70% of enterprise CMOs will identify accountability for ethical AI in marketing among their top concerns by 2025. From the same report, by 2027 80% of enterprise marketers will establish a dedicated content authenticity function to combat misinformation and fake material.

“These data points emphasise the fact that using technologies such as generative AI with safety, accuracy, and honesty in mind – to empower marketers is top of mind for organisations now and into the future,” added Moran.

SAS Customer Intelligence 360 development is tightly aligned with guidance from the SAS Data Ethics practice.

The pillars of the strategy for safely and responsibly introducing generative AI technology into marketing environments include:

• Prioritising data privacy – no sharing of sensitive company and customer data with the models.
• Maintaining human oversight – AI-generated content should always be reviewed and approved by humans.
• Creating interpretable and transparent output – it should always be clear to marketers how an AI algorithm arrived at its conclusions and recommendations.

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As an example, the South African healthcare industry has recently been wrestling with allegations of bias in the algorithms medical schemes use to detect fraud, waste, and abuse. Most recently, an interim report by an independent panel raised concerns about racial discrimination against black medical service providers. 

“We view such allegations seriously and understand the concerns about algorithmic bias. At SAS, we are wholly commitment to ethical AI,” said Essie Mokgonyana, Country Manager and Sales Director for SAS in South Africa.

SAS is recognised for its AI and advanced analytics solutions that equip healthcare organisations with the tools to effectively manage medical costs, identify potential fraud, waste, and abuse, and make higher-value referrals to regulators and law enforcement.

Today, these capabilities have never been more crucial. As AI continues to transform how medical schemes process vast volumes of claims, the necessity to ensure transparency, fairness, and impartiality in AI systems is paramount.

“SAS develops AI technologies with a focus on transparency and interpretability. This means we can explore what goes into our models, understand why they make certain decisions, and critically, ensure they are free of bias. Our goal is to enhance the health care industry with AI that is responsible, reliable, and ethical,” Mokgonyana emphasised.

SAS AI technologies are built on a foundation of strong governance and good data management practices. The use of such technologies in healthcare can provide a robust, data-driven approach to fraud detection, one that analyses all data, not just a sample, in real-time or as a batch.

However, Mokgonyana notes that AI, while powerful, is only as good as the data it learns from. Ensuring the quality of data is a responsibility shared by all stakeholders. SAS encourages a more collaborative effort from industry players in developing and refining these technologies, ensuring that AI can truly serve its purpose of advancing patient care and protecting resources for the greater good.

In the wake of recent developments, SAS is dedicated to helping the local industry navigate these challenging issues. To this end, the company offers SAS Payment Integrity for Health Care, which uses advanced analytics combined with embedded AI and machine learning algorithms to detect fraud and reduce false positives, all while optimising payment integrity.

“SAS’ solutions are engineered to provide a consolidated view of fraud risk, identifying linkages among seemingly unrelated claims, allowing healthcare organisation to prevent major losses early. We believe that with transparency, constant improvement, and stringent ethics in AI, we can create an environment of trust and fairness in healthcare,” Mokgonyana added.

SAS remains committed to empowering healthcare organisations with the advanced analytics and ethical AI tools needed to secure a fair and inclusive healthcare future for all South Africans.

Only 40% of marketing executives in EMEA are very confident that their company’s current Customer Experience (CX) strategies are capable of winning and retaining customers.

With high quality products and affordability rated as some of the most important drivers of loyalty, more must be done to digitise the customer journey.

This is according to the findings of an international Consumer Experience study conducted by SAS, one of the world’s leading providers of analytics and artificial intelligence (AI) solutions, in partnership with the CMO Council.

The challenge is to orchestrate a great customer experience that hits all the right notes: from digital self-service to meaningful personalisation, privacy and trust, as well as seamless omnichannel, including a ‘hybrid’ blend of physical and digital experiences. Consumers want a frictionless, rewarding experience when buying great products at affordable prices despite volatile geopolitical and macroeconomic trends.

For EMEA businesses, the majority are working on accommodating existing digital and physical engagement models (87%), balancing personalisation and privacy (87%), adjusting to supply chain issues (81%), reacting to customers in real-time with personalised interactions (73%), and managing the frequency and volume of customer interactions (70%).

The biggest problem with implementing the CX strategy still is coordination between departments. Just 11% of EMEA marketing executives believe their company is well positioned in this area, while just as few study participants (11%) attribute sufficient maturity to their company when it comes to executing Consumer Experience measures against the backdrop of a completely transformed digital infrastructure.

Leveraging augmented, virtual, extended, or mixed reality had the lowest number of mature ratings across all 13 of the CX capabilities.

The technologies that are predominantly invested in are marketing analytics, AI, and machine learning, according to approximately two-thirds of the survey participants in each case.

Marketing attribution and technology for measuring success play an important role, as confirmed by 55% of respondents in EMEA.

Given how third-party cookies are on the way out, respondents across EMEA indicated that they plan on using contextual targeting (44%) and ad experimentation and testing (44%) to track and target customers with programmatic advertising. What is interesting to note is that 79% of EMEA marketing executives believe that the role of hybrid CX that combines physical and digital experiences will be an important requirement in the next 12-months.

Many consider this to drive personalisation, innovation, and customer engagement. However, it is much easier to deliver hybrid Consumer Experience on top of a completely transformed digital infrastructure that provides a 360-degree view of the customer and their experience with the brand.

And yet, fewer than 1 in 7 marketers say they have a mature digital infrastructure. The good news is that brands are working hard on this.

The complete study ‘Cracking Tomorrow’s CX Code’ is available for download here.

Teachers4DataAnalytics will also form part of a bigger SAS driven programme focused on secondary education and provide a bridge/feeder for the company’s successful Global Academic Program in partnership with local universities.

Despite one-third of South African university graduates and almost 60% of learners who successfully complete grade 12 failing to find jobs and meaningful employment, a massive skills shortage in technical sectors persists.

With economies digitalising rapidly, creating roles for entrants with data analytics and statistical skills, SAS became increasingly concerned that school learners locally are largely unaware of the existing and emerging potential vocations that can offer them exciting and gainful employment opportunities and future-proofed career paths.

Andre Zitzke, Manager: Global Academic Programmes in Africa for SAS, says: “We are exceptionally proud of and excited about the Teachers4DataAnalytics program as this is the first program of this nature, scale and size focused on secondary schoolteachers and learners to be launched in a SAS operational region outside of the US. The aim is to empower teachers with the knowledge and tools to be better placed to reach more students, encourage their curiosity and provide them with exposure on practical applications behind the curricula in STEM subjects, so that learners become more informed on the careers available to them in the digital and data driven age.”

The Teachers4DataAnalytics programme was conceptualised by Professor Delia North, former Dean and Head of the School of Mathematics, Statistics and Computer Science, for the College of Agriculture, Engineering and Science at the University of KwaZulu-Natal (UKZN) in South Africa. Professor North has been highly influential in statistics education in the country, having served as the Chair of the South African Statistical Association Education for 17-years and played a seminal role in defining the statistics content of the school curriculum since statistics was first introduced into the school curriculum in 2002.

For the Teachers4DataAnalytics program, Professor North teamed up with Professor Christine Franklin, a world renowned expert and leader in teacher training, to optimise the uptake of statistical concepts at school level and produce a booklet that would guide teachers in their training to bring renewed insight into learning from data that is both modern, yet takes into account the complexities of teaching in a developing country with less than ideal infrastructure and access to laboratories for teaching.

SAS DataFly is the software of choice in this program, as innovative use of the versatile software will allow the teacher to use SAS DataFly despite constraints and realities of teaching in less affluent schools.

Additionally, the program is defined as a scalable initiative that will be taken to other universities across the country – and later across the continent – to help connect schools, universities, industry and government in creating a talent pipeline for a digital future. The series of teacher training workshops that will be held at universities across the country will culminate in a SAS DataFly poster competition for teachers.

The inaugural teacher workshop was held at UKZN on 13 August 2022, with more than 30 mathematics and science teachers from 26 schools around the province. Speakers on the day included Professor North, Professor Franklin and Miss Nombuso Zondo, a young lecturer in Statistics at UKZN, who profoundly presented the SAS Data Fly workshop.

Following the success of this launch, the team behind the Teachers4DataAnalytics program will take the workshop format on a roadshow to the University of the North-West and the University of the Western Cape in the coming months before the year’s end.

Professor North says school curricula require students to learn how to perform statistical calculations, but the time available to complete the year’s work limits learners’ ability to engage with the context of such calculations or their usefulness beyond school.

“This ‘dry’ method of presenting the curriculum does little to encourage curiosity and passion about statistics, which plays a role in reducing the number of candidates entering technical undergraduate qualifications and, ultimately, the number of university graduates who can fill important roles in a modernising economy. The sustainability of the skills value chain depends on learners being aware of career opportunities before leaving secondary school.”

In particular, she feels that any initiative that exposes learners to the enhancement of career opportunities in line with the needs of the modern workplace should be pursued with great focus. “Teachers are ‘change agents’ and can have a fundamental influence in this regard,” says Professor North.

According to Zitzke, SAS’s historical roots in academia are part of the reason the company is so involved in connecting talent with industry. “It’s not enough to get involved at universities because we need to generate passion for STEM careers among learners so that they can make informed choices about what they want to study at tertiary level. We need teachers onboard to do this, because the passion will be created in the classroom,” he says.

The Teachers4DataAnalytics program invites high school teachers to not only attend the workshop in which they will build further instructional capacity, but to participate in a competition where they will use SAS’s DataFly technology to bring statistics to life in a data exploration.

“We have considered the South African context in which many school learners will not have access to computer laboratories or mobile devices. The free SAS DataFly visualisation tool can be run on the teacher’s single laptop and learners can record data written by hand on cards that can then be fed into the tool to build graphs and analytics in real time. When learners watch the data fly in and populate histograms or scatterplots, that is where the magic happens,” says North.

“These projects will require learners to capture data from their everyday lives, such as their activities or emotional states through the course of a day, to connect data analysis with the real world. Seeing it in action, with the capability to add fun features like colour and emoji customisations, heightens engagement with calculations and demonstrates their usefulness in understanding the world around us. Each class will then create a poster showing how they’ve analysed the data,” North adds.

“It is our genuine hope that exposure through such projects will captivate the curiosity of learners on the process and purpose of capturing data for analysis, while introducing the skills that learners will need once they reach the time for a professional career of their choice, whether that is business, academic or any other field,” says Zitzke.

The coordinated Teacher Empowerment Workshops will leverage thought leaders from the South African Statistical Association Education Committee and the American Statistical Association to provide enriching learning material on SAS DataFly as the foundation technology, as well as additional material intended to stimulate discovery in the classroom.

The Teachers4DataAnalytics program also aligns well with other SAS sponsored talent connection programs such as the annual Women in Analytics and Dudes in Data events, which aim to connect top school talent with SAS flagship universities. There are also a series of Grade 11 learner recruitment events that will serve as data exploration exercises at UKZN and UWC.

The Forrester Wave: Anti-Money-Laundering Solutions, Q3 2022 evaluates the industry’s 15 most significant anti-money laundering (AML) technology providers, assessing and ranking them based on 26 evaluation criteria.

As criminals grow more sophisticated in their money laundering methods, financial services organisations like banks and insurers rely on ever-more advanced AML solutions to detect financial crime.

Notably, SAS Anti-Money Laundering attained the highest score of any evaluated vendor in the “current offering” category.

The AI-powered anti-money laundering software solution scored 4.85 out of 5 overall and received the highest possible score in 12 of the category’s 13 criteria, including:

• Users and roles
• Watchlist management and screening
• Rules-based scoring and alerting
• AI/machine learning-based scoring and alerting
• Internationalisation, currencies, and reporting
• Scale

The influential research and advisory firm’s assessment of SAS’ current AML offering notes that it “provides an overall robust solution.”

“Key management for encrypting data is explicitly configurable,” the report continues. “Rules-based and AI/ML based risk scoring is nice and functional. The solution can also provide rule recommendations. Workflow for model building is also functional and intuitive. Case management screen customisation and usability is superior.”

According to a global AML study by SAS, KPMG and the Association of Certified Anti-Money Laundering Specialists (ACAMS), one-third of financial institutions have accelerated their adoption of AI and machine learning for AML compliance since the COVID-19 pandemic began. Moreover, those that have deployed these advanced AML capabilities are seeing tremendous benefits.

“While anti-money laundering compliance expectations have increased due to more complex regulatory priorities, AI and machine learning are delivering on their promise of making AML programs more automated, efficient and effective,” said David Stewart, Director of Financial Crimes and Compliance at SAS. “It’s not hype or hyperbole. SAS has helped financial institutions achieve more than 90% model accuracy, reduce false positives by up to 80% and realise a twofold improvement on their SAR conversion rate.”

Financial services sectors have a crucial role to play in the ongoing development of African countries and are priority sectors on African Continental Free Trade Area (AfCFTA) Trade in Services agenda. In support of this, each country needs to ensure that it has strong regulation in place and in line with best practice principles set out by the global watchdog.

Stephan Wessels, SAS Head of Customer Advisory for South Africa, said: “The cost and consequences of financial crimes and illicit finance is a plight that emerging economies in Africa cannot afford – and especially as markets become increasingly globalised in the digital era.”

Earlier this month the head of South Africa’s National Treasury expressed that the country is taking the global anti-money laundering regime seriously – and that the country intends to have addressed any regulatory weaknesses in its money laundering controls by the end of the year.

“This is certainly a great step forward for the country – and financial services providers will need to ensure that their business and practices are in keeping with updates to the regulation,” said Wessels.

“We are very proud to be names a Leader in anti-money laundering solutions by Forrester. This is a testament to our advanced market solutions that are built to fight against money laundering and illicit finance with AI, machine learning, intelligent automation and advanced network visualisation – and to meet the challenges and risks in an ever-changing operating environment.”