What is generative AI exactly? It refers to AI systems that can generate new content, rather than just classify, match, or modify existing content. Examples include models such as GPT-3 from OpenAI and Claude from Anthropic. They can produce remarkably human-like text, code, images, and more from simple prompts. 

Three key areas where generative AI could benefit life insurers come to mind immediately: 

• Automating policy documents and paperwork: One of the less glamorous but crucial functions in insurance is producing reams of policy documents and paperwork. Generative AI excels at generating text for contracts, reports, regulatory filings, and more. This could significantly reduce the manual effort currently required for documentation. Concise, accurate paperwork helps cut costs and ensures compliance. 

• Speeding up and enhancing underwriting: Underwriting is time and labor intensive, relying on assessing applicant medical records, lab results, and health history. Generative AI can be trained on industry data to automatically summarize and extract insights from medical documents to help underwriters. This saves time and reduces human bias and errors. Fewer back-and-forth delays improves customer experience. 

• Creating personalized customer interactions: Each customer interaction—whether a policy explanation, renewal notice, or claim assistance letter—represents an opportunity to build trust and loyalty. Generative AI can generate customized communications tailored to the customer’s policy, background, and previous interactions with the insurer. This level of personalization promotes higher engagement and satisfaction. Insurers should note that sensitive interactions are still best handled by human customer service agents. AI is best used for personalizing the mundane-yet-necessary interactions that make up the bulk of policy servicing.

Generative AI can be an assistant to many roles and functions. As with any new technology, there are risks to be evaluated regarding data privacy, security, and bias in the AI systems. However, the potential productivity and innovation gains make this an area worth exploring and monitoring closely. 

CIOs have a unique role to play in responsibly piloting generative AI to transform life insurance for the better. If you’re curious on how to get started, you can read our report on the top 5 questions CIOs have about GenAI, or contact me directly.

The post The Promise of Generative AI for Life Insurance appeared first on Datos Insights.

The report’s key findings include the following: 

• Cyber range solutions provide an effective method of upskilling cybersecurity personnel: Classroom training is limited in teaching critical cybersecurity defensive techniques. Adding the gamification elements that cyber ranges offer increases knowledge retention, builds reactive event muscle memory, and provides necessary realism training through live-fire ranges.  
• Cyber ranges are changing the landscape of cybersecurity education: Certification courses have limitations in practically applying curricula. Cyber ranges offer real-life exercise training with a virtually limitless array of attack defense and proof of concept scenarios. Cyber ranges push past the theoretical to the practical.  
• Cyber ranges available for all sized organizations and budgets: The cyber range market offers 58 providers ranging from large in-house platform deployments to cloud service models.  
• Cyber ranges rapidly evolve: First-generation cyber ranges were mostly cybersecurity labs that teach through step-by-step instructions. Users no longer want that experience; they desire the gamified versions where they solve challenges and have a hacker’s point of view.  
• Cyber ranges improve first responder effectiveness: Cyber range training improves memory retention by 75%, compared to 5% through traditional learning methods. 

Participants of this report represent startup and scaleup vendors that subjected their cyber range platforms to 200 points of company and product evaluation scrutiny. This report is a buyer’s guide for organizations seeking a cyber range solution. 

The six vendors scored in the report include: 

• Aries Security 
• ATCorp 
• Cloud Range 
• CYBER RANGES 
• Field Effect 
• Security Innovation 

These vendors represent 10% of the known commercial cyber range solution providers. 

The difference between last place and first place was 12%, with the average score reaching 86%. Two vendors—Cloud Range and CYBER RANGES—achieved best-in-class. However, all participating vendors offer strong cyber range solutions.  

Final Thoughts

To learn more about the cyber range market, check out my report Cyber Range Solutions: Market Landscape, February 2024. I would love to hear how you have leveraged cyber ranges to improve training; drop me a line here to share your thoughts. If you want to keep up with my blogs on related IT security issues, go here. 

The post Which Are the Top Cyber Range Solutions? appeared first on Datos Insights.

Early Stage Investments 

Much pioneering work with generative AI involves experimentation without clearly defined products or tangible deliverables readily meeting capitalization criteria. During these early R&D phases, costs may be more appropriately expensed rather than capitalized as assets. 

Uncertainty in Useful Life   

Useful life estimates for generative AI solutions prove extremely difficult to reliably quantify given the brisk pace of advancement in this space. Whereas conventional policy admin systems typically follow 5+ year depreciation models, AI platforms and their rapidly evolving capabilities often render existing features and functionality obsolete within months. To account for this uncertainty, insurers may find it beneficial to review useful life assumptions annually. 

As Alok Bhargava, a partner at EY commented: “ For Insurance organization to evaluate the useful life of generative AI solutions can be a complex task due to the swift pace of development in this field. Today’s use case, built around a single model, might require the integration of multi-model in immediate future owing to rapid technological advancements. Given the heightened uncertainty regarding the lifespan of these AI solutions, it’s advisable for Insurance carriers to expense, instead of capitalizing their investments in generative AI.” 

For example, in just the year since the introduction of ChatGPT 3.5, numerous enhancements have been added that could obsolete solutions built around 3.5’s original capabilities. Alternatively, future solutions might apply several LLMs working in concert.  

Ongoing Commitments  

Insurance CFOs should also note that relative to conventional on-premise systems, generative AI solutions often require considerably more ongoing investments in maintenance and continuous training. 

In summary, while Datos Insights sees extraordinary value potential in generative AI, we urge insurers to take a prudent financial planning approach when allocating resources to these transformative and rapidly evolving technologies. Please reach out for additional perspectives on AI investment strategies. 

The post The Challenges of Capitalizing Generative AI Investments appeared first on Datos Insights.

Cyber hackers have had decades to perfect their craft, and their attacks show no signs of relenting. An underbelly of over 100 software-esque companies design Hacker-as-a-Service and Ransomware-as-a-Service products to challenge the cyber industry complex. These attacks will only become more sophisticated as these underground organizations build their own large language models and incorporate AI.  

Experts and cybersecurity leaders at Datos Insights Financial Crime and Cybersecurity Forum agreed that malicious actors are becoming more sophisticated. Many are trending towards evasion techniques like living off the land, i.e., using native system administration tools to compromise emails and exploit money. This lack of detectable malware means that cybercriminals can act in their own time, taking CISOs and risk professionals out of the driver’s seat.  

Another factor contributing to attacks like phishing and extortion is a culture of shame that exists in many organizations. Cybersecurity should not be a punitive exercise, and this attitude can disincentivize resources from reporting attacks. What’s more, the responsibility for cyber defense does not rest on employees alone, and the ability for an attack to take down an entire organization with one wrong click is a problematic vulnerability.  

Cyber leaders agreed that “defense in depth,” or multiple layers of security response, is crucial heading into 2024. This approach requires foundational pieces such as backups and multifactor authentication, plus monitoring, plus preparation. The goal, as one panelist pointed out, is to be able to take a punch.  

Yet, as another panelist noted, the approach of defense in depth is also “expense in depth.” Security organizations need to prioritize where to spend and maximize return on investment, given a limited budget. Simplifying operating environments and building a best-of-breed tool stack are sizeable pieces of the puzzle. Resilience will come from broad coverage, not necessarily one tool or a set of tools that can do it all.  

Cyberattacks are becoming easier for malicious actors to execute, which means they are becoming more difficult for cybersecurity teams to get ahead of. To discuss ways to skate ahead of the puck and ready organizations against these threats, feel free to reach out to me at sdalwin@datos-insights.com or Tari Schreider at tschreider@datos-insights.com.

The post Cyberattacks Ahead: A Difficult Prospect appeared first on Datos Insights.

WAAPs can address bot detection, application security, API protection, DDoS mitigation, firewall, and many other aspects of PCI DSS. The figure below is an abstract view of how a single integrated solution, such as a such as a WAAP solution, can address many risks to the payment card ecosystem. 

PCI DSS 4.0, published on March 31, 2022,  is one of the most important and impactful releases to date. This release addresses some of the most critical architectural, control, and design risks organizations face when accepting and processing payment card transactions. It requires compliance with 64 new requirements by March 31, 2025. Thirteen require compliance immediately for organizations opting for version 4.0 assessments.

However, some good news is that they’re related to improved documentation. The broad scope of this release has caused 90% of PCI DSS decision-makers to be concerned with meeting the deadline.

This version marks the first time PCI allows an organization to decide how best to comply with the standard. However, the burden of proof will be on the organization to demonstrate the effectiveness of its approach. PCI has also moved from snapshot control compliance to continuously monitoring security posture to prove risk management effectiveness and outcomes. Cybersecurity and fraud management are emerging as a fused discipline as an acknowledgment that the two are inexorably linked. This release will challenge organizations to transform their current approach to protecting cardholder data and focus on risk outcomes, not passing assessments. 

PCI DSS version 4.0 allows organizations to phase compliance over two years in three stages. Owing to the complexity of changes, the PCI Council allows one more year than previously for versions 2.0 to 3.0. The first stage is effective now and includes 13 new requirements that must be included for all organizations accessing version 4.0 of the required PCI DSS Report on Compliance or Self-Assessment Questionnaire. Stage 2 takes effect on March 31, 2024, upon the retirement of the current 3.2.1 version. Beginning April 1, 2024, all assessments must be under PCI DSS 4.0. The third and final stage requires the 51 best practices in place by April 1, 2025.

To learn more about how WAAPs can aid in complying with the PCI DSS version 4.0, check out my latest report, Understanding and Preparing for PCI DSS 4.0. I would love to hear how you intend to comply with the new version of PCI DSS; drop me a line here to share your thoughts. If you want to keep up with my blogs on related IT security issues, go here.

The post Are WAAPs the Answer to Complying With PCI DSS 4.0?  appeared first on Datos Insights.

■ マネーロンダリング対策の課題
昨今、金融犯罪に関するコンプライアンスに準拠するためには、複雑な手順が求められている。AIなどを使ったシステムによるスクリーニングも強化されているが、システムで判断が付きかねるケースはも増加しており、金融機関にとって精査作業の効率化が大きな課題となっている。

・対象ケースに関する情報（送金元/送金先に関する様々な情報）は、社内外の複数のシステムにばらばらに存在しており、多様なデータベースやWebサイトにアクセスし情報収集しなければならない。

・情報を収集した後、それらがどのような関連を持つのか（犯罪につながるのかどうか）明確でないケースがほとんどであり、調査を深めようとすればするほど時間がかかる。

・システムで抽出したアラートには擬陽性（問題のない取引であるのに疑義があると誤認してしまう）も多く、結果人手による精査が必要なケースが増加している。機械学習を使ったスクリーニング・システムも開発されているが、まだ明確な効果は見られないようだ。

このため、金融機関の担当者は忙殺されており、精査の一貫性が損なわれたり、当局報告資料に不備が生じる等の悪循環に陥っている。

■ Lucinity社のLuci Copilot
2023年春にリリースされたLucinity社のLuci Copilotは、マネーロンダリングの精査業務を生成AIを活用して効率化するサービスだ。Luci Copilotは、社内のスクリーニング・システムで「アラート扱い」となったケースは関連する情報を社内外のシステムから収集し、1つの画面にまとめてスクリーニング担当者に提示する。

・ケースの概要は生成AIを使って「Summary of Insights」としてまとめられる。担当者はなぜこのケースが「アラート」となったかを即時に理解できる。

・Luci Copilotが収集した情報に対する質問をチャット・ボックスに入力すると、解説が提示されケースに対する理解が深まる（例：なぜビヘイビア・スコアが「ハイリスク」なのか解説がなされる）。

・疑念があり当局報告が必要な場合、該当項目をチェックしてレポート作成を指示すれば、レギュレーションに準拠した報告書が自動作成される。

これらの機能の結果、アラート1件を処理する時間は、従来の20－25%程度にまで削減できるという。また、金融機関がスクリーニング担当者を養成する期間も大幅に短縮できると想定されている。

■ 生成AI実用化の第一歩
Lucinity社は、2018年創業のベンチャー企業であるが、生成AIの活用には創業当初から取り組んできた。2023年に入ってマイクロソフト/OpenAIのGPT4を活用し、Azure上で稼働するLuci Copilotをリリース、金融機関からの評価は高いようだ。Datos Insightsでは、2023年9月に同社を「2023年度AMLインパクト・アワード（Best Financial Crime Investigation and Reporting Innovation部門）」に認定、その概要をレポート「The 2023 Impact Awards in AML」にまとめている。

生成AIの実用化はまだ始まった段階であり、Lucinity社はその先陣と言えよう。同社の発展に注目するとともに、本年は、様々な分野での生成AI実用化に注目しておきたい。

The post 生成AI活用でマネーロンダリング精査を効率化：Luci Copilot appeared first on Datos Insights.

In a recent roundtable meeting of the Datos Insights Insurance Special Interest Group on AI and Innovation, we explored the implications of this rapidly unfolding transformation. Attendees represented a diverse cross-section of life/annuity/benefits and property/casualty insurers eager to compare perspectives and action plans for leveraging leading-edge AI capabilities while mitigating risks. 

The Promise: Massive Productivity Gains 

Early adopter feedback remains overwhelmingly positive. Several participants highlighted the value being generated by AI applications. Use cases that streamline traditionally labor-intensive processes received particularly enthusiastic endorsement: tasks like reviewing new business submissions, extracting details from third-party reports, and summarizing claim adjuster notes, to name a few examples. 

“It can really take [a task that would require] hours and deliver condensed summaries in seconds or minutes instead. Our adjusters can brush up on key events in a fraction of the time before contacting policyholders now,” noted one executive. Their peers concurred, and some insurers shared that they are already seeing productivity improvements as a result. 

The Peril: Risk, Bias, and Regulatory Compliance 

However, AI is not a magic cure-all; insurers must approach it with caution. These tools, while promising, have hardly reached full maturity yet. Consideration must be given to regulations as well as risks like chatbots making unauthorized coverage recommendations. Existing biases in algorithms and training data need to be proactively addressed as well.  

Carrier leadership teams should maintain a balanced perspective. While still in the early days, most seem to believe productivity and customer experience gains currently outweigh the risks posed by thoughtful AI adoption. Yet an undercurrent of healthy caution pervades. As one attendee noted, “This is all moving incredibly fast, and we must be responsive while avoiding recklessness.” 

The Prognosis: Strong Leadership and Enterprise Governance 

Getting governance right may not be as glamorous as deploying AI capabilities, but it’s what separates winners from losers. Governance and risk management practices must evolve to keep pace with rapid application advances already underway within insurance organizations. Moreover, those processes require ongoing oversight rather than once-and-done enactment, given the speed of change industrywide. 

Carriers should incorporate AI strategically into their existing risk, compliance, and IT checks and balances while staying flexible since environmental dynamics keep shifting significantly. The ability to audit model transparency and explainability will be just as crucial as thoroughly scrubbing training datasets. 

Harnessing AI’s immense potential for insurance while steering clear of the pitfalls necessitates asking tough questions together as an industry. We remain committed to helping insurance leaders find the right answers to unleash innovation responsibly. If you would like to join one of our upcoming discussions or learn more about our research, please visit our website or reach out to me at cgeaglone@datos-insights.com.

The post The Promise and Peril of AI and Innovation in Insurance appeared first on Datos Insights.

Yes, AI will transform the insurance sector. But the biggest disruption will come from how people leverage AI to become far more productive. The insurance professionals who embrace AI as a tool, especially generative AI, will thrive – even without waiting for their company to act. 

Generative AI refers to models that can produce novel content, predictions, and recommendations. Unlike narrow AI designed for specific tasks, generative models can apply learning across domains. Leading generative AI techniques include image, video, text and data generation. 

AI alone still cannot replace most jobs. While generative models are rapidly advancing, general artificial intelligence comparable to flexible human-level reasoning does not exist yet. We remain years away from AI that can fully replicate human cognition and consciousness.    

However, generative AI is increasingly amplifying human capabilities. Rather than directly replacing jobs, AI is empowering people to work faster with higher quality. For example, generative AI tools can help developers draft requirements, code, test and document their applications. Generative writing assistants assist underwriters in summarizing complex submissions and assist agents with marketing copy. Numerous use cases exist though many are beyond the reach of a casual user and require greater expertise and investment. 

Companies investing first in providing tools and upskilling for AI will have a competitive advantage. However, proactive individuals getting a head start with AI can outpace their employer’s training. People proficient in generative AI can become power users compared to peers only comfortable with traditional software.   

You don’t need management approval to augment your unique strengths with AI. Start exploring generative tools offering free trials and take online courses to guide your upskilling. As you become adept at using AI to work smarter, you’ll accomplish more than your job description requires and stand out from the competition. 

So while AI itself does not pose an immediate threat to take jobs at scale, people applying AI creatively surely will displace the skills of individuals who refuse to adapt. Your job security depends less on advances in artificial intelligence and almost entirely on your willingness to make AI part of your personal toolkit.

The post You Won’t Lose Your Job to AI. You’ll Lose Your Job to Someone Using AI appeared first on Datos Insights.

■ 生成AI/AI全般の活用
2022年末にリリースされたChatGPTの爆発的な広まりを背景に、2023年は世界中で生成AIの活用に関する論議が進展した。多くの企業では様々なトライアル/パイロットが実施され、生成AIの能力や限界、課題に対する認識も進んだ。

企業内での生成AI活用は、様々な試行が行われた結果、成果を上げているケースもあるが、社外サービスへの応用（顧客サポートなど）は、テクノロジーの限界が把握できない事やAI利用に関したレギュレーションが未整備なこともあり、今後の課題となっている。金融不正の分野では、AIを活用した不正検知の精度向上が期待されるが、犯罪者が不正攻撃のツールとして生成AIを活用することへの懸念が現実のものとなっている。

■ 犯罪者のAI活用と防衛手段としてのAI活用
サイバー犯罪者の情報共有環境であるダークウェブでは、FraudGPTやWormGPTと呼ばれる不正ツールが登場している。これらは、ChatGPTでは制限されている「不適切な質問」にも回答する生成AIだ。フィッシング・メールやSNSへの不正投稿原稿を簡単に作成できるだけでなく、ニセのWebサイト作成もサポートしてくれる。またプログラミングに対する深い知識がなくともMalwareを簡単に作成できる。その他キャプチャ認証に自動回答する画像認識ツールや、音声やイメージのサンプルからフェイク・イメージ/フェイク・メッセージを作成するツールも流通している。

一方、不正対策へのAI活用は生成AI登場前から始まっており、技術の進展とともに不正検知の精度向上や新しい不正パターンの早期検知、不正対策のソースコード自動作成、更には、今後登場するであろう不正の予想への応用が期待されている。また、正規の銀行口座やカード所持者が騙されて送金してしまう詐欺（オレオレ詐欺やロマンス詐欺など）の検知にも、AIの応用が始まっている。

■ 生成AI/AI全般の本格活用への準備
2024年、AIに関連した課題として以下3点を認識して能動的な準備を進めるとともに、経営陣がAI活用に関して適切な判断をできるよう継続的なインプットやトレーニングを進める必要があると考えるがどうだろう。
（1）金融不正/サイバー攻撃の手口の進化/変化のスピードアップ
（2）AIに関するレギュレーション動向
（3）生成AIの利用を前提とし、社内のガバナンス・ルールやプライバシー・ポリシーなどの見直し

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Given the crucial role of fintech vendors in the ever-evolving commercial banking sector, it was an opportune moment to synthesize and build on our existing fintech coverage to offer a refined focus on some of the companies and dynamics shaping the broader commercial banking and payments landscape. The report builds upon our popular quarterly Fintech Spotlight report series, in addition to reports assessing various components of the fintech market and our frequent conversations with banks and vendors. 

What’s in the Report

No report can claim to cover the entire fintech market: Estimates suggest it includes over 26,000 companies distributed globally. This report offers a large but by no means comprehensive list of fintech vendors helping to shape the modern commercial banking sector. 

Shaping can mean many things. In this case, we mean companies pushing the envelope with innovative products, vendors impacting emerging or underserved markets, solutions emblematic or representative of broader trends, and services or solutions that integrate the cutting edge of technological advancement. 

We aren’t endorsing any of the companies included in the report, but we do think the companies are exciting and worth checking out. The majority of them report less than US$25 million in annual revenue and are typical of what is commonly thought of as a fintech. A select minority of the companies are larger to help illustrate phases of growth and maturation of fintech vendor solutions. The product offerings of the featured companies fall within numerous product categories, including payments, cash management, lending, core and digital banking, and data management and analytics.

In sum, our team hears about and looks at hundreds upon hundreds of vendors every year, which we distilled down to 350. For each company, we provide the following information:

• Company website
• Product category
• Go-to-market strategy
• Headquarter location 
• Product description
• A brief explanation of why we included the company 

Beyond the indexed and categorized list of 350 vendors, the report also provides market commentary on a few of the following topics:

• Drivers of adoption 
• Funding patterns 
• Implications of the Regional Banking Crisis 
•  Scandals 
• Fintech marketplaces 
• Pain points within specific product areas 

Why it Matters

The fintech marketplace in commercial banking continues to evolve rapidly. FIs and large legacy financial services vendors now more than ever need to understand opportunities available to them through partnering fintech vendors and recognize potential challenges that disintermediating fintech-based products and services pose. FIs seeking to understand the dynamics behind disintermediation will find the report and follow-up conversations with the teams’ analysts especially valuable. 

Recognizing new technological innovation, revenue opportunities, and shifts in how financial services meet or exceed customer needs can also guide FIs and larger vendors on new product development. In many regards, the fintech vendor marketplace within certain product areas shows us what sorts of solutions are growing in popularity, how expectations are changing, and where innovation is headed.

The current market is increasingly challenging for smaller fintech vendors to navigate due to higher interest rates, increased investor skepticism toward the technology sector, and growing competition. A foundational challenge many newer fintech vendors are experiencing right now is that FIs, larger vendors, and even businesses are deploying more stringent due diligence toward third-party vendors. 

A greater emphasis on due diligence translates to more failed onboarding and longer durations to reach revenue. Moreover,more startups are experiencing shorter cash runways and less interest from investors. This dynamic is at the core of why many fintech vendors are going out of business or merging with larger firms to stay afloat. This report provides critical insight into competitive intelligence, possibilities for differentiation, and an overview of what sorts of products and services are succeeding in a turbulent environment.

How Datos Insights’ Clients Can Leverage the Report 

The report provides readers with market commentary on some of the critical factors impacting the contemporary fintech marketplace, such as interest rates, funding, and drivers of innovation within cash management, core and digital banking, data management and analytics, lending, and payments. The index of 350 fintech vendors offers insight into companies that are shaping the broader market through product developmentand intervention within emerging and underserved markets.

Clients of Datos Insights can benefit from this report’s content in several of the following ways:

• Refine existing fintech research and partnership strategies 
• Recognize existing market indicators within various lines of business 
• Understand companies that are reimagining financial service products or developing cutting-edge solutions through new technologies 
• Learn about revenue opportunities, client needs, and new areas of product innovation 
• Access a range of companies ideal for potential partnerships 

Not everything can fit into a report. Hundreds of companies we looked at were not included. Only a fraction of the team’s collective knowledge on product areas, drivers of innovation, and data on end-user preferences could make it into this report. 

There is no “one size fits all” approach when it comes to refining a strategy for product development, fintech partnerships, or how to compete with rising expectations for best-in-class services. As a result, some of the best insights garnered from this report are likely to come from follow-up conversations with members of the Commercial Banking and Payments practice. 

For clients or prospective clients seeking to learn more about the report or schedule a follow-up advisory call for a deep dive on any subject related to the report’s findings, please reach out to me at bnestor@datos-insights.com.

The post Overview of 350 Fintech Vendors Shaping the Commercial Banking and Payments Landscape appeared first on Datos Insights.

BlackBerry’s IoT technology is used in 235 million (June 2023) cars. Blackberry (formerly Research in Motion) was founded in 1984 in Ontario, Canada. The company has made an impressive transition from an interactive pager and smartphone provider into a top-40 global cybersecurity company. 2023 revenue is US$656 million, with 64% coming from cybersecurity. Its US$1.4 billion acquisition of Cylance Inc. mostly comprises its cybersecurity business. 

The company faces challenges, including the increased cost of selling cybersecurity vs. IoT solutions, two customers that account for more than 10% of its receivables, and US$371 million in debt interest and principal payments. Gross revenue for cybersecurity solutions declined by 23%. Presently, the cost of cybersecurity sales consumes 42% of respective revenue. Separating cybersecurity and IoT businesses makes a lot of sense, but ultimately, this analyst believes that BlackBerry will need to exit the IoT business to really focus on the higher-margin cybersecurity business. 

What’s Next?  

The company appointed John J. Giamatteo, the head of its cybersecurity business, as CEO, a precursor of company focus. Mr. Giamatteo has a rich background in cybersecurity, including time spent at McAfee and AVG Technologies.  

BlackBerry’s endpoint protection products are generally well-liked in the market. BlackBerry sold off substantially all non-core patents in March 2023 for US$170 million in cash and a royal agreement that could bring upwards of US$900 million, so it will have some money to invest. With the right direction, this company could be a top 20 cybersecurity vendor in the next three years. 

Contact me here to share your thoughts on BlackBerry’s future. If you want to keep up with my blogs on related IT security issues, go here. 

The post BlackBerry Realigns to Focus on Cybersecurity appeared first on Datos Insights.

This could immensely accelerate insurance IT projects and lower application development costs. However, while the underlying deep learning advances make this feasible soon, businesses may need to temper expectations around maturity and manage risks.

Key Advances Building Towards This Capability

In the last few years, neural networks have achieved impressive results in analyzing images and video, understanding natural language, logical reasoning, and generating code. Combining these strengths, AI agents could potentially map UI flows and data logic captured from application demos onto the underlying codebase.

Vision systems can now identify on-screen components, actions, and responses. Language models allow translating text and speech into structured knowledge. Reasoning algorithms can map between conceptual representations. And code generation techniques have progressed from simple programs to complex architectures.

Assembling these capabilities into an integrated solution is the next step several AI teams are eyeing. The payoff for automated coding at scale is substantial, evidenced by heightened VC interest in startups like Anthropic working on this technology.

Emergence of New Competition

However, the same capability allowing rapid application development from demonstrative videos could significantly disrupt established technology vendors serving the insurance industry. As barriers to entry into solution creation lower, new players leveraging AI coding could launch competitive alternatives faster and cheaper.

Incumbents may suddenly find their extensive software IP and armies of developers losing differential value. Industry expertise encoded into proprietary systems over years risks being commoditized by AI-fueled startups. Emerging “no-code” tools empowering non-programmers present an existential crisis for current market leaders.

Mitigating Strategies for Protection

Legacy technology companies can adopt defensive innovation strategies to protect market position. Investing in internal AI coding labs could help to compete with external offerings. Acquiring promising startups early in capability development further aids maintaining competitive parity.

Strategic partnerships with AI platform companies, academic labs, and integrators also help monitor, evaluate and ultimately deploy automated coding alongside human developers. Proactively evangelizing tools, use cases and integration blueprints engenders ecosystem support.

Carefully navigated, AI-based software automation could expand markets for new solutions while responsibly balancing job impacts across the insurance technology value chain.

The Path Ahead

Neither hype nor despair is currently warranted when evaluating automatic coding from app demos. What insurance technologists should track is how incremental breakthroughs in causal visual reasoning and software synthesis stack up year-on-year. 

When machine learning models reliably demonstrate the ability to understand complex logic from limited samples, such as a video of an application screen flow, the software development process will be transformed providing an order of magnitude improvement to software development productivity. Continued research paper releases and technology validation experiments will illuminate how close this future actually is.

More advances are required before the core systems vendor community is disrupted and the best option for most insurers remains a vended solution. If you’d like to discuss your policy admin, claims or billing core system needs, please contact me or Mitch Wein to continue the conversation.

The post Core Systems Vendors Beware: AI-Enabled Code Generation Is on the Way appeared first on Datos Insights.

GenAI is more than just a complex technology evolution with serious potential to become ubiquitous in work and social life. It also has an esoteric philosophical battle happening behind the scenes. Proponents of GenAI argue that it will impact the ultimate fate of humanity; critics say it will primarily impact the fate of Silicon Valley.

Concepts such as artificial general intelligence (AGI), the technological singularity, effective altruism, the future of robotics, transhumanism, and so on make for great conversation topics (and subject matter for countless think pieces and blog posts). Yet, these topics are all secondary to the mundane day-to-day realities within banking that GenAI is well placed to transform. These more esoteric aspects of the philosophical underpinning of GenAI development are nonetheless fascinating. They will likely have a major influence on how this technology develops over the next few years. The recent drama at OpenAI and the ousting (and subsequent quick return) of CEO Sam Altman are one example.

At the center of this philosophical battle is a conflict between two schools of thought. The first is “effective altruism,” a modern rehashed form of utilitarianism that quickly goes to weird places, such as colonizing space with sentient artificial life and a strong concern over existential risks to humanity (e.g., rogue AGIs). Proponents here include Elon Musk and now disgraced and jailed Sam Bankman-Fried. The second is the “effective accelerationist” camp, sometimes abbreviated as e/ACC. Those on the e/ACC side argue that technology development and AI, in particular, are social equalizers. They believe the only moral stance is to develop these capabilities at top speed with minimal regulation so as not to impede progress. In the battle over OpenAI’s direction and management, the E/ACC proponents, backed by the significant funds and power of Microsoft, have essentially won out. Thus, the speed of GenAI development will only increase, barring any stringent regulations.

Much of the debate on GenAI focuses on these long-term and often morally grey areas, which risks distracting from the real-world impact these technologies can have in the here and now. Banking, in particular, is a key industry for GenAI, wherein routine and mundane manual processes that nonetheless remain technical and complex are major pain points for financial institution solution providers and their end users. Banking is, if anything, a practical industry. Outside of investors, it is less likely to focus on long-term projections of space colonization, AGI, or the technological singularity. Instead, banks want help with streamlining processes, generating efficiencies, and solving day-to-day problems. Areas such as payment exceptions handling, cash forecasting, reporting and analytics, and enterprise content management are the primary focus of banks today in deploying these capabilities.

GenAI providers who can help banks manage day-to-day pain points and ultimately build a clear business case will see significantly faster growth and deployment. If GenAI vendors do not focus on meeting these needs and solving actual business challenges, then many may dismiss GenAI as yet another technology hype cycle that they can ignore for the time being. Banks will need to focus on investigating and, in some cases, developing these practical use cases in conjunction with their technology partners, who will undoubtedly have less of an understanding of the needs of financial services.

Fortunately, the GenAI world is expanding rapidly, and the range of options and technology partners is advancing at a rapid pace. GenAI does not exclusively belong to OpenAI and Microsoft. Newer capabilities, such as small language models, and new players like France’s Mistral (an AI firm that achieved a double unicorn valuation of over US$2 billion on a Series A funding round), as well as development platforms like AWS Bedrock, and NTT Data, all grow the potential for banks to develop and launch GenAI solutions that can solve their practical problems.

As outlined in the recent Datos Insight Report Generative AI in Banking: Use Cases and Opportunities, less than a year after the public launch of ChatGPT, 60% of surveyed banks report they expect to launch GenAI capabilities within the next two years. That’s a shockingly high number for an industry typically seen as risk-averse and inherently conservative.

While the philosophical underpinnings of GenAI are fascinating (and frankly often alarming), banks and technology partners alike need to prioritize the day-to-day problems they face rather than get distracted by the esoteric hype and promises emanating from many. 

The post The Future of GenAI in Banking Will Be Determined by the Mundane, not the Radical appeared first on Datos Insights.

■ 流通業から学ぶ
ユーザー・エクスペリエンスの好事例として取り上げられるのがAmazon.comだ。Webサイトのナビゲーションや商品の選びやすさ、チェックアウトの簡単さなどがアマゾンに対する信頼感/安心感を生み、結果的に価格競争に陥らず多数のリピーターを獲得して事業を拡大してきた。アップルのiPhoneやそのApp Storeも同様と言えよう。

インターネットとともに成長してきた世代（ミレニアル世代/Z世代）が社会人となり、デジタル・サービスに対する期待値がこれまでの世代とは根本的に変わってきた。米国の大手金融機関では、デジタル時代の差別化施策はCX向上策でしかないとの認識のもと、各社がWebサイト/モバイル・アプリの改善を担当するデジタル・チームを立ち上げている。例えば、米国最大手のJPモルガン・チェイス銀行の場合、2016年に1500人体制のDigital for Consumer & Community Banking部門を発足させた（発足時の部門トップはアクセンチュアから、No2はYahoo.comから招聘）。

■ CX向上のために必要な道具だて
CX改善の第一段階では、Webサイト/モバイル・アプリのデザイン（ユーザー・インターフェース：UI）面の改善が行われたが、次第に顧客ニーズの変化や他社の新施策に対する迅速な対応が、CX競争で落ちこぼれない施策だとの認識が広がった。そのために必要となるテクノロジーには、以下などがある：
・アジャイル開発環境（API/マイクロサービス/DevOpsなど）
・顧客チャネルの同期化（Webサイト/モバイル・アプリ/店頭/コールセンターが顧客情報をリアルタイム共有できる情報インフラ）
・パーソナライゼーション推進（データ分析に基づくWebサイトの調整や商品提案など）
・マニュアル処理の排除/最小化による時間短縮（口座開設/ローン審査などの自動化）

これらを突き詰めた結果、データ・マネジメント環境の全面再構築やコアシステムの入れ替え（クラウド化）に進んだ企業もある。

■ CX対策に終わりはない
昨今、流通業でも金融業でも、顧客が求める品質の高いサービスとは、スピーディーでスムーズな対応と同義語であり、それがブランドに対する信頼感/愛着を生みロイヤリティを高めている。20世紀には人的リソースで良好なエクスペリエンスを提供することが行われてきたが、これを如何にテクノロジーで行うかが21世紀のDXだと言えよう。

更に、エクスペリエンス向上の対象は顧客に留まらず、社員（Employee Experience）や国民（Citizen Experience）に対しても必須だとの認識が広がりつつある。バイデン大統領も2021年12月に「政府サービスに対するCX向上で国民の信頼を回復するための大統領令」を発布、CX改善は連邦政府の行政改革の柱にもなっている。ITソリューション・ベンダーでも、機能の追加や設定変更がモバイル・アプリのような使い勝手で提供される次世代コアバンキング・システム等の開発が進んでいる。

自動車業界では、ガソリンエンジンから電気自動車への転換を見据えた変革が進んでいるようだ。デジタル・トランスフォーメーションに関する取り組みは、1950年代から始まったビジネスにおけるコンピュータ利用の大きな転換点のようにも思えるがどうだろう。

The post デジタル・トランスフォーメーションの狙いはCXによる差別化：米国企業の共通認識 appeared first on Datos Insights.

The predictions aligned neatly into an A-to-Z listing of 31 categories ranging from API protection to zero-trust architectures. But what were the top 10? A surprise to no one, AI-related predictions were number one. However, it balanced as either evil AI or good AI. Ransomware, although number two, was 25% points lower in importance. With this year being an election year, election fraud was number three. You can see the others below. 

Companies from which I gathered predictions included Check Point Software Technologies Ltd., ColorTokens Inc., Critical Start, CrowdStrike, CyberArk, Cybereason, Cybersixgill, Darktrace, F5, Field Effect, Google Cloud, Imperva, KnowBe4, McAfee, NordVPN, NortonLifeLock, Optiv, Outpost24, Palo Alto Networks, Proofpoint, Rapid7, ReliaQuest, Secureworks, Securonix, SentinelOne, Skybox Security, SonicWall, Sophos, Splunk, ThreatX, Trend Micro, WatchGuard Technologies, ZeroFox, and Zscaler.

Contact me here to share your 2024 cybersecurity predictions. If you want to keep up with my blogs on related IT security issues, go here. 

The post What Is the Cybersecurity Crystal Ball Telling Us? appeared first on Datos Insights.