Top 30 Data & Analytics Predictions for 2022

Following the tumultuous events of the last two years, 2022 is set to be a defining moment in the data and analytics landscape. While the initial impact of the pandemic may be behind us, its effect on digital transformation is only accelerating.

95% of organizations are still struggling to leverage their data to make meaningful business decisions. That’s why, in 2022, data and analytics technologies will continue innovating at an incredible pace. Pioneering technologies are opening up new opportunities for organizations seeking to gain greater decision intelligence in today’s data-driven world.

In the coming year, we expect to see enterprises leveraging innovative technologies in response to stricter regulations and a surge in data quantity. But what else does 2022 have in store?

Here are Quantexa’s top 30 predictions for 2022:

Anti-Money-Laundering (AML)

There will continue to be a steady – but increasingly significant and diverse – redirection of illicit finance away from traditional banks and other such institutions, and into cryptocurrencies/virtual assets.

#1. The general interlinkage between cryptocurrencies/virtual assets and fiat currency within the regulated and unregulated financial system will continue to rise, causing regulators and industry bodies globally to adapt their policies and enforcement focus.

#2. There will be a growing need for institutions providing financial services to integrate data seamlessly and transparently from non-traditional sources to support holistic monitoring/surveillance

Know Your Customer (KYC)

We expect to see a greater focus on perpetual or trigger-based KYC. This is part of a continued move to improve the customer experience by enabling straight through processing of low-risk changes to the customer record, and to improve the real time detection of suspicious behavior.

#3. There will be a trend towards greater transparency around Ultimate Beneficiaries of Organizations (UBOs) and a continued push to get more UBO registers created and mandated to facilitate the KYC process and improve customer experience, especially for multi-banked organizations. Organizations will seek to leverage the right technologies to make access to registers as seamless and as automated as possible to support the evolution from periodic to trigger-based KYC and risk assessments.

#4. There will be a shift towards a proactive KYC due diligence process to downstream risk mitigation. Institutions that continue to move their focus to customer and counterparty behavior at a network level right from onboarding will be more able to spot risk indicators earlier in the customer lifecycle.

#5. Greater transparency around onboarding, monitoring, and managing the risk of digital currencies and other non-fungible tokens (NFTs) will be key. Digital currencies are fast becoming another widely used asset class that brings with it additional risk of fraud and money laundering. Institutions will need to be able to provide a holistic customer view that includes KYC information for all assets and products to better understand AML and sanctions risk. 

Credit Risk

We expect to see a greater focus on holistic risk management, leveraging advanced analytics technology to expand the focus beyond direct borrower assessment to other key players in the ecosystem. 

#6. There will be a growing need to include climate risk and Environmental, Social and Governance (ESG) factors into credit decisions and portfolio monitoring to support customers’ transitions to net Zero.

#7. We will see a move to dynamic and real time alerting to identify soft signals or warning flags which, when in combination pose an emerging risk.

#8. There will be a focus on utilizing advanced technologies such as network analytics to better identify connected risks and proactively avoid corporate fraud at the origin, or at an earlier stage of portfolio monitoring.

Fraud

Across the scams, procurement and supply chain, and lending fraud typologies, helping banks take appropriate action to protect customers and eliminate false positives is key for 2022 – and advanced analytics will be central to this. <

#9. Scams and social engineering attacks have risen aggressively through the pandemic and continue to pose an unanswered threat to the banking industry. In 2022, creative solutions to these challenges will unlock advances in real-time intervention and decision engines that are backed by greater context.

#10. We will observe an increased focus across well known risk domains such as Fraud, Bribery & Corruption on analyzing supply chain data for both direct contractors and for the tiers of sub-contractors beneath. We will also dive into newer risk domains such as ESG, modern slavery, human trafficking, or adversarial capital.

#11. As the economic backdrop continues to settle and government backed relief programs roll back, there will be a lot of opportunistic fraud. We will see credit teams and fraud teams working closely together to determine those who ‘can’t pay’ versus ‘won’t pay’, and to anticipate where risk arises based on evidence that can be seen in the cashflows.

Government

In the coming year, a key trend for governments will center on the opportunity for faster, more proactive enforcement. As challenges related to virtual currencies, tax enforcement, and the pandemic continue to grow, agencies will need to innovate to keep up with the emerging threats.

#12. The continued rise of virtual currencies has proven a challenge for governments combatting tax evasion and financial crime. But all signs point to the continued adoption of virtual currencies in the mainstream and sophisticated criminal groups. Most government agencies investigating financial crimes have adopted blockchain data and investigation tools to trace transactions, but the real challenge will be linking the “on-chain” identities to the real world.

#13. In May 2021 in the U.S., the Internal Revenue Service (IRS) outlined a compliance agenda initiative that seeks to close the “tax gap.” For its core processing of tax returns, the IRS currently relies on a 1960s-vintage IT system called the Individual Master File (IMF). A significant focus of the plan is to increase the data analytics and machine learning tools to improve visibility into taxpayers’ income and tax liability.

#14. Globally, another area of concern follows the Pandora Papers offshore account leak. To accurately identify complex tax evasion schemes, governments will need to utilize advanced analytics technologies to hold accountable those who abuse the tax system through the use of offshore entities and structures.

#15. The Covid pandemic has rapidly accelerated the government’s need to adopt innovative technology. Beyond the displaced workforce, there has been an exponential increase in crimes related to Covid relief funding. Governments will need to continue investing in tools that can prevent fraudulent behavior and investigate networks engaged in criminal behavior.

Telco

With the rapid rollout of 5G and IoT solutions, the volume and inherent value within the data is set to increase considerably.

#16. Data as the primary differentiator in business performance and customer experience, and is the asset that differentiates one business from the next. Data will enable a trifecta of creating new business, protecting customer experience, and managing the cost base. There will also be an increasingly important role played by data in the ESG space – swathes of disparate and inconsistent data will have to be connected and curated to drive good and tackle bad.

#17. The shift to cloud amplifies the case for effective and practical master data management. As almost every Telco moves its data (and more of its decisioning) into the cloud, the challenges of data siloes, and insufficient compute and storage resources diminish. Removing these impediments will reveal the need for investment and focus on how data is managed to help data scientists deliver maximum value to their users.

Insurance

Data is at the core of 2022’s Insurance technology predictions – and advanced analytics will all be leveraged in the coming year.

#18. In the wake of Covid-19, a sustainable recovery will follow and a stance on ESG is at the top of the change agenda. Insurers must implement and maintain ESG standards and protocol, while ensuring their book of business reflect these practices, too.

#19. In 2022, insurers will focus more technology investments on areas that drive product diversification, better customer insight and higher quality customer engagement. Omni-channel engagement will fuel this, and solutions that can enable insurers to provide customers with a “personal touch” will prevail.

#20. Data aggregation and collection will be paramount in order to have a competitive edge. Wearable technologies will improve workplace safety and workers’ compensation loss ratios.

#21. Data breaches and data thefts have resulted in vast amounts of personal information being leaked, and this is only going to rise in the new year. Knowing the identity of the individual will be crucial, especially for fraud typologies.

Graph

Scalable data graph technologies will start to become a core part of emerging data fabrics in leading organizations.

#22. Graph’s flexibility in integrating disparate data sources and building contextual awareness will see it increasingly used for enterprise-scale data integration alongside traditional data warehousing and data lake approaches. Scalable data graph technologies will start to become a core part of emerging data fabrics in leading organizations, enabling a more adaptive data infrastructure that distributes meaningful data to where it’s needed.

Artificial Intelligence (AI)

2022 will see explainable and transparent AI becoming a key theme. As organizations seek to roll out real production solutions based on AI decisioning, the need for explainability will come into sharper focus.

#23. Organizations will rediscover “garbage in = garbage out”. There will be an emphasis on the quality of the data, how it is enriched and the need for data in context as an input into AI models. Graph approaches and the associated need for single views of entities will play a great role as input into AI algorithms.

#24. As organizations seek to operationalize AI, there will be a growing need for models back-tested in batch environments to be seamlessly deployed into real time production environments, with the reliance on IT department intervention.

Machine Learning (ML)

Attitudes to Machine Learning (ML) will continue to mature. Stakeholders are becoming more aware of ML failures, which will allow them to develop a greater understanding of what is possible, and impossible, using ML.

#25. Decision-makers will cease to consider models as crystal balls providing unfathomable insight, but instead understand the power of models in achieving specific measured objectives from available data. Increasingly models will be set realistic tasks, where the objective is achievable based on the available data, with emphasis being put on obtaining a sufficient breadth of data if necessary.

#26. There will be a consolidation of approaches to ethical AI and ML. There is now widespread awareness of the risk of unfair decisions resulting from incautious use of AI, and this year is likely to see the convergence of approaches to combat such risks. It’s likely some of the following will become widespread approaches:

• A greater importance put on transparent, explainable models
• Appropriate use of AI assisted decisions, where operators receive the insights from AI models but make critical decisions themselves.
• Provision of wider data to algorithms, allowing models more quality information for building robust estimates, thus avoiding reliance on inappropriate proxy measures such as economic background or gender.

Data Management

Overcoming the challenge of legacy transformation will be a key theme for CDOs in 2022. But Master Data Management (MDM), Customer Data Platform (CDP) and Analytical data joining are providing solutions to data challenges.

#27. Data platforms are evolving, and many existing technologies are not coping with the volume and diversity of data needed for digital transformation and automated decisions. In 2022, IT leaders will continue to question the need for multiple platforms which each bring data together in different ways, and as a result will seek an intelligent solution to master data management.

#28. Organizations are realizing they need a plan to migrate from legacy applications onto new digitally native services. Replacing legacy applications is a challenge – but one that can be solved by creating a single view across data and enabling controlled migration to new services.

#29. Data assets are the missing link in the data value chain. To make best use of data and technology investments, CDOs and CIOs will be looking at how data assets can be created and published out to consumers across the enterprise.

#30. Batch data flows can be a huge issue for organizations that want to be responsive to customers, because data is often 24 or 48 hours old at the point of use. To maintain trust with customers and meet expectations for consistent omni-channel communications, critical data flows need to be moved to real-time to enable the joining up of data and instant decision responses.