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Why Plug-and-play Should Replace Embedded Finance as the New Norm
Financial Services Review | Wednesday, December 14, 2022
Embedded finance allows business models to operate more effectively by making working capital more efficient or making payments easier
FREMONT, CA: Embedded finance is fundamentally the fusion of the financial services and digital worlds. After being held apart for a long time, its combination creates several possibilities for innovation with significant practical benefits.
Embedded finance makes it possible for business models and the customer experiences that go along with them to function more successfully, for instance, by improving working capital efficiency or simplifying payments. Customers, embedders, financial institutions, and the overall addressable market are all expected to benefit from embedded finance.
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Customers
When SMEs can access credit more easily due to the extensive data already on the systems they now use, such as accounting software or e-commerce platforms, this has significant value. For underserved consumers, a similar opportunity exists: by combining applications that deal with a wider element of their lives, such as their mobile phone contract with new financial services, these customers can be economically serviced with more than just basic financial services.
Embedders
Three key advantages accrue to innovators who incorporate financial services into their businesses. First, they can profit from the financial transactions that are currently conducted on their platform. Next, they draw in new clients and deepen their engagement by enhancing their product with fresh, turbocharged features. Finally, the outcome increases client lifetime value and retention.
Financial Institutions
Financial services provided by banks are distributed and used more widely and are used by more people thanks to embedded finance providers. The bank that offers the underlying instruments will benefit if embedded finance providers let more businesses, for instance, issue more debit cards to consumers.
The market has substantial and growing potential. By 2026, the US market for platforms and enablers will have more than doubled, reaching USD 51 billion in total revenue across payments, lending, banking, and cards from 2021. Over the same period, the transaction value of embedded finance will increase from US$2.6 trillion to USD 7 trillion.
Current Embedded Finance Climate
The current state of the art for constructing embedded finance solutions was to leverage banking-as-a-service (BaaS) programming interfaces supplied by banks for things like opening accounts or issuing cards (BaaS). BaaS is a difficult task for most innovators because it takes a long time to execute and costs a lot of money.
The BaaS concept will become more complicated as authorities scrutinise it more closely. EML Payments temporarily stopped accepting new customers when the UK regulator, the Financial Conduct Authority (FCA), expressed concerns about it in November 2022.
More regulation for the US BaaS market is on the way, according to recent hints from the Office of the Comptroller of the Currency (OCC). A clear instance of regulators taking action against a particular bank's use of BaaS models is highlighted by the public filing of an agreement between the OCC and Blue Ridge Bank. Although this is an alarming concern for Blue Ridge, it offers other fintech companies and banks in the market a road map on how to stay on the right side of regulation. Consequently, the end user's protection is enhanced and increased.
Customer Onboarding: identification verification, background checks, and risk evaluation. Innovative businesses can use financial data security tools to adhere to data security standards like PCI-DSS. Components, such as biometrics, satisfy the legal requirements for strong customer authentication. Keep track of any significant client financial activity and the customer's approval of terms and prices. Connection to transactional systems and record-keeping financial systems (e.g. ledgers). Coordination between various financial institutions, contractors, and service providers
Few, if any, innovators who want to benefit from integrated finance are willing to deal with the duties listed above. However, releasing innovators from the majority of these responsibilities must be complemented by enough freedom to foster creativity and deliver seamless client experiences.
The bank is reassured that these delicate issues are being handled to its standards rather than being delegated to the innovator, who generally does not want or have the capacity to do so.
Five Key Principles that Plug-and-play Finance Solutions Should Adhere
Software-only Responsibility: Plug-and-play financial solutions should be equally as easy to set up and maintain as any other software programme. Abstraction from financial protocols: Plug-and-play finance solutions should aggregate and encapsulate the numerous fine-grained sequences of steps, interactions, and data exchanges necessary to achieve them while abstracting away specific technical protocols associated with financial technology. Instead, they should expose functionality in high-level intentional terms, such as onboarding a customer, issuing a card, and enabling funds transfer.
Contextual Adaptation: Plug-and-play finance solutions should not offer functionality and procedures that are one-size-fits-all, whether they are concerned with risk management, compliance supervision, end-customer charges, approval workflows, etc. They ought to have deployment context adaptability.
Maximum Allowed Access to Financial Data and Controls: As permitted by data security, end-customer privacy, and financial regulation, the end-ability customer's to effectively delegate and withdraw access, and the risk of unintentional or malicious action by the innovator or third parties to the end-detriment, customer's plug-and-play finance solutions should offer maximum visibility over financial data and maximum control over financial actions.
Minimum Constraints Over Non-financial Data and Controls: Plug-and-play financial solutions should give the inventor as much freedom as possible to create any component of the solution they choose, as long as doing so won't jeopardise the solution's integrity or subject them to financial regulation.
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The expansion of cyber risks, the increasing sophistication of economic crimes, and digitization are driving a profound upheaval of the global financial ecosystem. Financial security services are now essential for safeguarding private information, online transactions, and customer confidence. The market for economic security services is growing quickly as financial institutions, fintech companies, and businesses are under more pressure to protect assets and adhere to strict rules.
Criminal actors use increasingly sophisticated methods, including social engineering, synthetic identity creation, and AI-powered attacks, to exploit vulnerabilities in economic systems. The implementation of AI in financial security services is a game-changer. AI-powered platforms can monitor vast volumes of economic data in real time, identifying unusual patterns and anomalies that may signal fraudulent behavior or a cyber breach. ML algorithms learn from historical data to refine detection models and reduce false positives, a common challenge with legacy fraud detection systems.
AI Adoption and Driving Forces
Several pivotal factors influence the growth and expansion of the financial security services market. From online banking and mobile payments to cryptocurrency and decentralized finance (DeFi), consumers and businesses now rely heavily on digital platforms for financial management. While these innovations offer convenience and speed, they also introduce vulnerabilities such as identity theft, phishing attacks, ransomware, account takeovers, and payment fraud. Organizations invest heavily in cybersecurity tools and managed services to meet legal obligations and minimize risk.
AI-driven financial security services, such as those offered by Pivot Financial , leverage natural language processing (NLP) and behavioral biometrics to detect insider threats, authenticate identities, and monitor transactions for anomalies. These tools enhance compliance accuracy, reduce human error, and streamline customer onboarding while maintaining high security standards.
Financial institutions are moving away from on-premises infrastructure in favor of cloud-native security platforms that offer scalability, rapid deployment, and centralized threat monitoring. Cloud security providers offer advanced threat intelligence and APIs that enable seamless integration with existing banking systems. The tools aggregate data from global cybersecurity feeds, dark web forums, and internal logs to deliver predictive insights about potential threats. Such proactive defense mechanisms enable financial institutions to stay ahead of attackers rather than simply reacting to incidents.
Industry Impact and Strategic Importance
Financial security services are no longer limited to fraud prevention. They now encompass a wide array of applications across the economic value chain. AI-powered identity verification tools ensure the legitimacy of account holders during the onboarding process. The tools use biometric verification, facial recognition, document scanning, and real-time identity checks to reduce onboarding fraud and meet compliance needs. In transaction monitoring, AI models detect irregular behavior, such as large fund transfers, rapid account withdrawals, or international money flows that deviate from a user's historical pattern.
Customers benefit from smoother onboarding, fewer disruptions due to fraud, and secure multi-channel experiences. For institutions, AI-powered financial security reduces operational costs, minimizes losses, ensures regulatory compliance, and builds long-term trust with stakeholders. Small and medium-sized enterprises (SMEs) are increasingly adopting managed financial security services. Lacking in-house cybersecurity teams, SMEs rely on third-party providers for endpoint protection, secure payment gateways, identity management, and compliance reporting.
The proliferation of fintech companies and digital wallets in emerging markets also drives the need for robust financial security services. As these regions digitize, the threat landscape expands, making AI-enabled security infrastructure necessary for financial inclusion and economic growth. Governments and central banks in Asia, Africa, and Latin America are promoting public-private partnerships to strengthen national financial cybersecurity frameworks.
Challenges in Implementation and Solutions to Overcome Them
AI systems are as effective as the data on which they are trained. Institutions must adopt data governance frameworks that ensure access to high-quality, anonymized, and up-to-date datasets. Continuous learning and model retraining should be integrated into the security infrastructure to keep pace with evolving threats. Financial regulators require transparency in decision-making in cases of customer rejection, fraud claims, or compliance issues. Many ML models lack explainability, making it difficult to justify decisions to regulators or customers.
Many financial institutions operate legacy systems that are incompatible with modern AI solutions. Hybrid IT strategies where AI tools operate in tandem with legacy platforms while gradually migrating to modern infrastructure can ease this transition. Cybersecurity risks associated with AI tools themselves are another concern. Malicious actors can exploit vulnerabilities in AI models or use adversarial techniques to manipulate outputs. As such, institutions must secure AI pipelines, monitor for model drift or corruption, and implement robust validation and testing protocols.
Implementing AI-powered financial security requires substantial investment in infrastructure, skilled personnel, and ongoing maintenance. Financial institutions address this by partnering with managed service providers, investing in AI upskilling programs, and leveraging open-source AI frameworks to reduce costs. Data privacy and ethical considerations play a role. AI systems that analyze sensitive financial data must adhere to privacy laws and ethical standards. Institutions must implement robust data encryption, clear consent protocols, and comprehensive audit trails to ensure the protection of consumer data and compliance with relevant laws. ...Read more
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AI's ability to handle multiple variables simultaneously gives it a significant edge over traditional forecasting methods. The predictive power helps analysts and investors make more data-driven decisions, reducing the risks associated with market volatility and improving the accuracy of long-term investment strategies. Market sentiment plays a crucial role in equity research. Understanding how the market perceives a company or sector can significantly influence stock prices. AI-driven sentiment analysis tools can gauge market sentiment in real time by analyzing news articles, social media, financial blogs, and other public sources of information.
AI algorithms can quantify market sentiment by analyzing the tone of earnings calls, company statements, and public opinions on social media platforms. It gives equity analysts an up-to-the-minute view of how the market reacts to specific companies, sectors, or events. Analysts can better assess market conditions and identify potential investment opportunities or risks before they become widely known. Human bias is a common challenge in traditional equity research, where subjective judgment or emotional reactions may cloud decision-making. AI-powered tools help mitigate this risk by relying on data-driven, objective analysis.
AI-driven tools automate these processes, ensuring precision and enabling analysts to focus on interpreting results and forming strategies based on solid data. AI's ability to analyze large datasets and uncover hidden patterns allows for more personalized and granular research. Equity research analysts can use AI to tailor their analysis to specific investment strategies, risk profiles, or sectors of interest. By analyzing multiple data points simultaneously—such as company performance, market sentiment, and macroeconomic indicators—AI-driven platforms can generate customized investment recommendations that align with specific goals. ...Read more
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One of the significant challenges in managing the treasury of an enterprise comes in terms of the complex nature of global markets, primarily due to the involvement of different jurisdictions and, therefore, regulatory environments, fluctuations in currency, and geopolitical uncertainties. It makes mandatory more robust practices in the risk management system, thus using highly advanced financial instruments and analytics to anticipate and mitigate risks.
The pace at which technologies change has transformed treasury functions, especially fintech solutions, with new cash management techniques, advanced forecasting, and improvements in transaction processing. The challenge here is integration into legacy systems and data security. Treasury teams must balance the technologies with efficiency without hurting data integrity or security.
Some are starting to be noticed in the merger of treasury management. Another trend is liquidity management. For operations to be successful and growth actualized, organizations realize that liquidity has to be assured. With the importance realized, organizations have "dedicated greater attention and optimization to cash flow forecasting, real-time availability of information about cash positions, and optimizations in working capital.". Treasury professionals are now using advanced analytics and cash management solutions to respond quickly to changes in the market with better liquidity strategies.
Sustainability is becoming a significant factor in treasury management, too. More demanding stakeholders and their demands for more sustainable corporate practices mean that treasury teams must incorporate ESG considerations into investment and financing decisions. This is an opportunity but a challenge; these organizations want to stay invested in sustainability-related financial strategies. Treasury professionals embracing this shift can offer much in improving their organizations' reputations and building socially responsible investors.
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Many Registered Investment Advisors (RIAs) and Outsourced Chief Investment Officers (CIOs) continue to rely on manual methods for handling data and reporting workflows related to their alternative investments. As firms expand their operations and acquire additional clients, the difficulties associated with scaling these manual processes become increasingly pronounced.
Challenges to Scaling
The following are the three primary challenges that we frequently encounter:
Human Resources:
In the realm of alternative investments that necessitate manual performance reporting, many Registered Investment Advisors (RIAs) and Outsourced Chief Investment Officers (CIOs) believe that growth can only be achieved by increasing the workforce. As the client base grows, so does the volume of alternative investments to manage, requiring additional personnel to handle manual reporting processes.
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Technology Adoption:
Recently, technology vendors and service providers have introduced a range of solutions developed to reduce the challenges associated with manual reporting; however, these solutions remain highly fragmented. A universal solution for the unstructured data related to alternative investments has yet to be established.
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Scalable Processes:
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