VIEWPOINT

2023

THE INTERSECTION
OF AI & FINANCIALSERVICES
How AI is supercharging
embedded finance

AUTHORS
Arjun Vir SinghIn our evolving digital landscape, artificial intelligence

Michael Eiden(AI) is driving innovation across multiple sectors. It

Dr. Mohammad Nikkar

has now intersected with embedded finance, which
weaves financial services into nonfinancial platforms,

Michael Bateman

enhancing user experiences and streamlining

Iyad Halabi

processes. AI has the potential to lift embedded

Muhammad Danial

finance to its fullest, offering tools to combat fraud,
curate personalized experiences, and manage risks.
In this Viewpoint, we shed light on the interplay
between AI and embedded finance, sharing current
applications, future trajectories, and the manifold
challenges.

ARTHUR D. LITTLE

VIEWPOINT

Embedded finance, the seamless integration offinancial services into nonfinancial platforms,- Square, a financial services platform,
leverages machine learning (ML) formarks a monumental shift in financial services.

underwriting and extending loans to small

Its transformative potential is undeniable. The

businesses bypassed by conventional banks.

global embedded finance market is projected- E-commerce platform Shopify embeds the AI

to reach US $350 billion by 2025. Its symbiotic
integration with AI, another transformative force,is accelerating embedded finance’s momentum.
While AI’s historical contributions to finance,such as algorithmic trading and fraud detection,
remain foundational, it is poised to support
embedded finance’s growth and address someprowess of payment service processor Stripe,
offering individualized solutions, which
amplifies the sales potential for its merchants.

- Retail behemoth Amazon relies on AI todissect data and curate personalized loanoffers anchored in sales history.

of its challenges.

These examples only scratch the surface.

AI is evolving into the linchpin of the embedded

Figure 2 reveals the spread of AI’s influencefinance revolution, thanks to advancements inacross embedded finance.

generative AI (GenAI), large language models

AI’s potential to expedite and amplify embedded(LLMs), and deep learning. GenAI can create

finance cannot be understated. This synergy

offerings tailored to individual users. LLMs can

is not just beneficial; in many respects, it ishandle customer queries, manage data, andindispensable.

predict market trends with greater accuracy(see Figure 1).

The essence of embedded finance is its
comprehensive integration with nonfinancial

AI has moved from facilitating to creating,platforms, ensuring that users can access andinnovating, and shaping finance in real time. Itsutilize financial services without disrupting

profound impact on embedded finance is rapidly

their primary engagements. AI elevates thisexpanding, and some might argue that we arefeature; its advanced personalization capabilities

only beginning this journey.

discern user needs before they become apparent.

A plethora of applications already exist, which
may predict AI’s ability to drive wider adoption

embedded finance: applications of AI in embedded
Figure 1. The ofmultifaceted
finance

Figure 1. The multifaceted applications of AI in embedded finance

RISK MANAGEMENTCUSTOMER JOURNEYPERSONALIZATIONFRAUD DETECTION

Intelligent algorithms bolster
risk assessment by detecting
inconsistencies &preemptively identifying
potential challenges; suchcapabilities can be used inlending & insurance
underwriting

AI amplifies value proposition
for customers & brings agility
to applications, accelerates
underwriting & refines credit
assessments, culminating in
swifter, more favorable
decisions; this streamlined
experience bolsters customer
satisfaction, fostering loyalty

Embedded finance thrives on
personalization; AI dissects
customer behaviors, enabling
services to be tailored toindividual needs

AI dissects vast data sets
to unearth anomalies;
continuous scrutiny oftransactional data, coupledwith behavioral analytics,
fortifies the system against
fraudulent activities inreal time

01

02

03

04

Source: Arthur D. Little

Source:D. Little OF AI & FINANCIAL SERVICES
THEArthur
INTERSECTION

2ARTHUR D. LITTLE

VIEWPOINT

Figure 2. Applications of AI across major embedded financemodalities
Figure 2. Applications of AI across major embedded finance modalities

EMBEDDED
PAYMENTSEMBEDDED
LENDINGEMBEDDEDSAVINGSEMBEDDED
WEALTH MGMT

MULTIFACETEDSOLUTIONS

Smooth payment
processing, fraud
detection & security
measures

Judicious credit
underwriting, risk
evaluation & prompt
loan sanctions

Bespoke savings
strategies &streamlined deposits

AI-poweredrobo-advisors todispense financial
advice & automate
portfolio governanceAI across an array ofembedded financial
services, spanningpayments, lending,risk mitigation &holistic personal
finance management

Source: Arthur D. Little

Source: Arthur D. Little

For example, with data analytics, AI could
instantly offer a specific financial product, likean installment plan or short-term loan, to a

EMBEDDED FINANCE:BEFORE & AF TER AI

user shopping online for a high-end electronic

Embedded finance could substantially accelerate

product.

its growth through its marriage to AI, whose

The world of finance revolves around trust. While
traditional financial institutions have built thistrust over decades, embedded finance solutions

wide-ranging applications facilitate its adoption
and deepen the intimacy of the user experience.
Three broad application categories stand out:

don’t have this luxury of time. This is where AI

1. Smoother customer journeys. AI can gocomes to the fore. Its predictive analytics andbeyond processing to amplify the value

fraud-detection capabilities verify that financial

proposition for customers. It brings agility totransactions are secure, transparent, and in theapplications, accelerates underwriting, anduser’s best interest. An e-commerce platformrefines credit assessments, which culminates

offering financial services will gain trust faster if

in swifter, more accurate decisions. This

its AI-backed systems demonstrate transparency,

streamlined experience bolsters customer

minimize errors, and preemptively address user

satisfaction and fosters loyalty. Augmented

concerns.

by AI-powered chatbots, customer support

Moreover, AI’s scalability, especially withadvancements like LLMs and GenAI, means it
can adapt in tandem as embedded finance grows
and diversifies. It can process larger data sets,

becomes more fluid, queries are promptly
addressed, and the reliance on humanintervention diminishes, creating an efficientcustomer experience.

interact with other systems, handle increasingly
complex user queries, and cater to diverse
financial needs without needing additional
resources.

THE INTERSECTION OF AI & FINANCIAL SERVICES

3

ARTHUR D. LITTLE

VIEWPOINT

2. Improved risk management. AI has elevated

Table 1 shows some use cases and illustrates

the accuracy of risk assessments, which

improvements made possible through AI. We

redefines financial inclusivity, making

see the trend of supercharging embedded financeservices accessible to those previouslywith AI continuing as new and more innovative

marginalized by conventional financial

use cases emerge in the future (see Figure 3). We

systems — whether due to demographics,

anticipate developments in all four categories

unconventional credit histories, or perceived

shown in Figure 3, which may include:

risk. Realizing the untapped potential in these- Biometric authentication (customer

segments translates to substantial revenue

journey). A commitment to security

opportunities. AI can also enhance portfolio

is paramount, as financial institutions

management and optimization on the side

essentially outsource their customerof both the provider and the customer.

acquisition to embedders. Advanced biometric3. Faster, more accurate fraud detection. AI canauthentication, powered by AI, stands at thedissect vast data sets to unearth anomalies.

forefront of the shift toward user acquisition

Continuous scrutiny of transactional data,

through the embedder. This approach, which

coupled with behavioral analytics, fortifies theincorporates facial, fingerprint, voice, andsystem against fraud. Given that control aroundiris recognition, promises a stronger level ofcustomer acquisition often rests with thesecurity for all financial transactions.

embedding entity, this vigilant layer becomes
indispensable to mitigate provider-side risks.

Table 1. Examples of application differences with and without AI

Table 1. Examples of application differences with and without AI

APPLICATIONUSE CASE

Smoothercustomerjourneys

Application auto-fillComplex, time-consuming manual data input,& screening, KYC/KYB prone to user errors & higher abandonment rates

Automated pre-population reduces user effort, improves accuracy& converts better; preliminary screening increases efficiency

Customer service& support

Standard chatbot responses lead to potential
miscommunication & user frustration

Advanced LLMs provide informed responses, elevated customer
satisfaction & reduced escalations; chatbots use NLP tounderstand queries & generate responses

Insurance claims
processing

Manual, lengthy, error-prone, requires humanintermediaries

Organized process facilitates customer interaction, faster claims
resolution & more embedded experience

Credit assessment& loan approvals

Conventional, rule-based approach may resultin unnecessary rejections

More nuanced, individual assessment of creditworthiness may
broaden customer base within acceptable risk levels

Policy & productpricing

One-size-fits-all pricing can miss revenue
opportunities & misalign with customer value

Dynamic pricing strategies tailor offerings to individual risk
profiles & purchase histories

Savings & investment
adviceStatic offerings require user input, potentiallydeterring less financially savvy users

Analyzes user habits to offer savings suggestions, nudges &investment strategies & promotes user engagement & loyalty

Embedded personal
finance shopper

Basic, static advice based on generic data,
lacks real-time context & depth

Offers custom, on-the-spot suggestions based on user’s
immediate context

Loan underwriting

Static credit-scoring models marginalize
high-risk customers

Intelligent models incorporating multiple data sources makes
services accessible to those previously marginalized byconventional financial systems

Portfolio risk
management

Manual portfolio balancing & risk management

Intelligent risk management tools leverage internal & external
data points

Payment processing
Basic rule-based systems may fail to catch& transaction analysis sophisticated fraudulent activities, posing risks

Identifies intricate patterns & anomalies in real time, reducing
fraudulent transactions & enhancing security

Relevant
personalization

Improved risk
management

More effective
fraud detection

WITHOUT AI

WITH AI

Source:Arthur D. Little
Source: Arthur
D. Little

THE INTERSECTION OF AI & FINANCIAL SERVICES

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ARTHUR D. LITTLE

VIEWPOINT- Sentiment analysis in wealthtech (risk- Regulatory compliance and blockchain (riskharnesses natural language processing

to pinpoint requirements, ensure adherence,(NLP) to extract opinions and emotions fromand highlight discrepancies. The increase insocial media and news, offering a predictive

blockchain transactions requires scrutiny,edge for market trends and investment

which AI can accomplish. Its ability to detect

strategies. Coupled with LLMs that formulate

patterns and anomalies plays a pivotal role insummaries and reports, this technology is set

combating fraud.

management). AI-driven sentiment analysis

to revolutionize the wealthtech sector, bothwithin the realm of embedded finance andoutside of it.

- Voice-activated transactions (fraud
detection). The integration of audio

processing and LLMs transforms how we- Customized financial advice

interact with our finances. As audio-activated(personalization). The traditional paradigms

transactions become a reality, AI can employof robo-advisors and financial advisors arevoice biometrics to authenticate and securely

poised for a major shift. AI can tailor financial

verify users.

advice to an individual’s unique circumstances,including behavior, risk tolerance, income,and spending habits. This transcends thelimitations of traditional portfolio approaches.

-management). AI parses through regulations- Dynamic risk management and behavioral
analysis (risk management). Real-time risk

management is essential. By processing vast
amounts of market data, AI can instantly

Automated debt collection (riskevaluate and offset risk exposures and issue

management). AI makes error-prone, human-

timely alerts for emerging threats. In tandem,

led debt collection organized and efficient. It

AI’s behavioral analysis capabilities enhanceuses NLP to communicate with debtors andsecurity and user experience by identifying

curate feasible payment plans.

any deviations in typical user patterns.

Figure 3. Predicted expansion of AI use cases in embedded
finance
Figure 3. Predicted expansion of AI use cases in embedded financeImpactVoiceactivatedtransactions

Customized
financialadviceBiometric
authentication

Now

Dynamic risk
management& behavioral
analysis
Sentiment
analysis
Regulatorycompliance &blockchain

Advanced
forecastingfor financial
planning

Automateddebt
collection

Immediacy

Source: Arthur D. Little

Source: Arthur D. Little
THE INTERSECTION OF AI & FINANCIAL SERVICES

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ARTHUR D. LITTLE

VIEWPOINT- Advanced forecasting for financial

planning (personalization). As households- Automated customer service. LLM-poweredchatbots could provide more insightful,grapple with soaring costs, advanced

contextually aware customer support.

forecasting emerges as a beacon for financial

Chatbots decode complex user queries withplanning. AI offers sophisticated projections

remarkable accuracy, ensuring an efficientfor income, expenses, and savings by

and informed transaction. LLMs do more than

generating actionable advice. AI’s focus on

facilitate transactions: they offer real-time

personal finance management prepares users

assistance, anticipate user needs, and suggest

well for their financial futures.

alternate payment methods.

These advancements paint a promising picture
for the financial sector, ensuring that services- Personalized financial advice.

Personalization is at the heart of modern

are more efficient, secure, and deeply attuned

consumer interaction. LLMs, with their

to individual needs.

ability to process vast amounts of data andgenerate human-like language, can offer

ADDING LLMs IS NOTJ U S T A N I N C R E M E N TA LS T E P, I T ’ S A L E A P I N T O
A FUTURE

financial advice that feels truly bespoke. Thesemodels can sift through purchase histories
and preferences to help users confidently
navigate products and services. LLMs support
embedded lending with insights into optimal
repayment strategies and educate users on
proactive loan management, giving them atransparent, manageable experience.

EMBEDDED FINANCE’ S
LLM DIMENSION
Card-issuing platform Marqeta tapped OpenAI’s
LLM to speed up its launch of embedded financeapplications. The result, Marqeta Docs AI chatbot,
is a GenAI tool that allows customers to quickly
navigate the site. Customers can ask questions
and collect information and gain more knowledgeof the platform’s offerings. The tool also
addresses the process of embedding different- Regulatory compliance. LLMs can further
refine this task, simplifying the regulatory

process with their aptitude for understanding
detailed documents. While final decisions willinvariably involve human discretion, LLMs canmake the process faster and more robust. In
embedded insurance, which often involves
numerous policy details, LLMs, especiallywhen coupled with knowledge graphs, canguide users through the intricate clauses,
helping them comprehend coverage details.

categories of payment services. Marqeta is an

It’s evident that adding LLMs is not just an

excellent example of how embedded financeincremental step. It’s a leap into a future where

and AI are starting to merge and leverage LLMs.

finance isn’t just about numbers, it’s about

Our exploration of AI’s transformative impact

delivering a thoughtful, personalized user

includes another evolving dimension: LLMs suchas GPT-4. These models bring the potential to
drastically elevate embedded finance throughexperience. As embedded finance continues its
ascent, harnessing the capabilities of LLMs could
be its next big opportunity.

their advanced language-generation andcomprehension abilities. Established companies,
especially those focused on embedded finance,
can gain a competitive edge by adding GenAI
capabilities to their operational fabric, including:

THE INTERSECTION OF AI & FINANCIAL SERVICES

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ARTHUR D. LITTLE

VIEWPOINTCHALLENGES & E THICALCONSIDER ATIONS- Regulatory, compliance, and legal risks.

The transition from analog to digital brings
regulatory challenges. The trend points

As we continue our journey, it’s crucial to address

toward a localized approach to data storage

the challenges and concerns that inevitably

and sharing, which is hard to reconcile

accompany groundbreaking innovations. Despitewith the global nature of the Internet. The

the benefits, there are some real considerations

emergence of local privacy regimes posesthat consumers and providers need to grapple with:the risk of increased fraud as an unintended- Trust and transparency. The natureof embedded finance’s integration into
nonfinancial platforms faces skepticism andintensifies concerns about data privacy andsecurity. Furthermore, fraud has become aconsequence. Moreover, standards likethe Payment Card Industry Data Security
Standards (PCI DSS) add operational burdens.

- Operational risk. Operational challenges
are exacerbated by the rollout of real-

pervasive theme, making trust even moretime payments alongside AI tools, which

critical. In a digital economy, as highlighted

creates an environment conducive to fraud.

by the recent surge in AI-enabled frauds, theThe continuous evolution of data security

onus is on businesses to maintain this trust.

demands businesses to adapt, but without- Data security in the age of AI. AI canstrengthen cybersecurity, but its power
to process vast streams of data instills
apprehension. Financial platforms needclear industry standards, the pathway remains
ambiguous.

- Model risk, including hallucinations.
AI, especially deep learning models,to articulate their protective measures,

might produce unintended outputs or

especially given the predicted surge in“hallucinations.” Financial decisions are prone

payment-fraud losses, expected to exceed

to serious risk, as incorrect predictions can

US $40 billion by 2027.

lead to significant implications.

- The “black box” dilemma. AI decision-making
can jeopardize trust if those decisions lack

- Reputational risk. Failure to manage theabove can harm a company’s image, especiallytransparency; fairness and potential biases

in the sensitive realm of finance. A significant

are significant concerns, especially when

fraud event, caused by AI vulnerabilities, canfinancial decisions are involved.

erode customer trust and impact a company’s- Tackling algorithmic bias. Bias in AI reflects

standing.

societal issues; embedded finance’s mission

As we reflect on the promise of AI, it’s clear thatto democratize access is undermined if biases

the path forward will require balancing innovation

persist. Efforts like explainable AI (XAI) canwith ethics, efficiency with transparency, andoffer transparency.

capability with responsibility. As embedded
finance evolves, championing these values will beessential for widespread acceptance and success.

THE INTERSECTION OF AI & FINANCIAL SERVICES

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ARTHUR D. LITTLE

VIEWPOINT

RECOMMENDATIONS FORFINANCIAL SERVICESPROVIDERS & FINTECHSFinancial services providers and fintechs havepivotal roles that give them unique opportunities.
Here are several recommendations based on
our exploration of embedded finance and the

CONVERGENCE OF
AI AND EMBEDDEDFINANCE PRESENTS
A T R A N S F O R M AT I V E
OPPORTUNIT Y FOR
THE FINANCIAL SECTOR

intertwined roles of AI and LLMs:- Integrate, don’t just incorporate, thetechnology. AI is the new financial reality.

- Promote transparency and address

algorithmic bias. The black box nature

Financial services providers and fintechs mustof certain AI models and the potential for

integrate it holistically, making AI-driven tools

algorithmic bias make it vital for financial

and insights intrinsic to their operations. Goals

services providers and fintechs to champion

should include refining user experiences,transparency. Investing in XAI will show that

automating processes, and opening new

every financial decision made by an AI model

avenues to personalized services. Providers

can be understood, justified, and trusted byshould collaborate with potential embedders

the end user.

to create a more seamless customer experience.

- Forge meaningful partnerships with AI- Continual learning and adaptation. Finance,technology, and user expectations are in apioneers. While financial services expertisestate of flux. Providers should commit to learn

is paramount, an in-depth understanding ofand adapt, regularly updating their models in

AI is equally crucial. The realm of AI is vastline with new data, trends, and best practices.

and complex and relies on the expertise of
specialists to support a technically sound andethically aligned integration, especially given
the overall importance of transparency andfairness.

- Make data security a prime directive.

The convergence of AI and embedded financepresents a transformative opportunity forthe financial sector. Considering theserecommendations can position financial services
providers and fintechs at the forefront of thisrevolution, where they can drive innovation

Today, data is often equated with currency.

while upholding trust, transparency, and ethicalHowever, it’s more than an asset in the world

standards.

of embedded finance; it fuels ML models.
This data is critical to tailor services andits protection is nonnegotiable. Adequate
security thwarts breaches while building
trust. Firms must establish robust encryption,
stringent access controls, and a commitment
to respecting user privacy.

THE INTERSECTION OF AI & FINANCIAL SERVICES

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ARTHUR D. LITTLE

VIEWPOINTCONSIDER ATIONSFOR EMBEDDERS

FUTURE TRENDS& PREDIC TIONS

It is vital for nonfinancial businesses, orEmbedded finance, fueled by AI, has gained“embedders,” to grasp the nuances andenough momentum to place it at the forefront

opportunities. These businesses stand at theof the next technological revolution. Here’s aintersection of traditional industries and innovative

closer look at some anticipated trajectories

financial solutions, ready to redefine customer

and implications:experiences. Embedders must meticulously select- Peak personalization. AI analyzes user

their partners, as each offers distinct advantages:-

patterns, preferences, and histories and offers

Finance is heavily choreographed byfinancial solutions to meet individual needs.

regulations. From data protection to licensing,

Imagine insurance policies determined byregulatory norms shape the foundation oflifestyle choices or investment plans attuned

financial services. Traditional banks, given theirto personal aspirations.

long-standing experience, might appear tohave an upper hand. However, innovation andcompliance can coexist, provided there’s a deep
understanding of the environment.

- Businesses venturing into sectors likehealthcare or employment face intense
regulatory challenges. Whether it’s adhering
to regulations or ensuring compliance, thejourney is complicated. The confluence of legal
acumen and industry knowledge becomes
crucial. Collaborating with industry stalwarts
can demystify regulatory complexities.

- The brilliance of AI isn’t limited to enhancing
user experiences or predicting trends. Its

prowess in automating compliance processes
demonstrates its versatility. By comparing vast
data reservoirs against regulatory standards,
AI can make compliance fast, accurate, andefficient. Fintech visionaries Plaid and Forter
leverage AI to validate transactions swiftly,
simultaneously establishing legitimacy andthwarting potential fraud.

- Embedded finance is filled withpossibilities and challenges. By strategicallyselecting partners, understanding regulations,and leveraging AI’s multifaceted capabilities,
embedders can add trust, innovation, andunparalleled customer experiences to their- Redefined efficiency. Automation will

streamline processes, drive efficiency, andreduce costs. Wait times for loan approvals or
dispute resolutions will shrink, while precisionsoars.

- Financial inclusivity. Individuals historicallysidelined by rigid conventional banking criteria
will have better access and options. Alternative
data metrics and innovative business models
will make financial inclusivity a reality.

- Demystified financial algorithms. XAI willshed light on the intricacies of financial

algorithms. Customers will understand thereasons behind the financial decisions theyreceive, cultivating trust.

- Future foundations. AI, LLMs, blockchain,and edge computing will augment embedded
finance, offering secure, swift, andscalable solutions. The confluence of thesetechnologies will form the backbone of futurefinancial ecosystems.

- Strategic collaborations. Financial giants will
forge alliances with fintech and AI innovators,
combining traditional wisdom with modern
agility to elevate embedded finance solutions.

- Market dynamics and maturation.

Acquisitions will become commonplace. The

offerings. As the landscape evolves, therace to scale, coupled with access to vast data

fusion of technology, finance, and traditional

troves, will determine the victors.

business promises a future where boundaries
blur and possibilities are limitless.

THE INTERSECTION OF AI & FINANCIAL SERVICES

9ARTHUR D. LITTLE

VIEWPOINT- Regulatory shifts. Governments will

establish frameworks ensuring data sanctity,algorithmic fairness, and consumer protection.
These regulations might also trigger industry
consolidations.

- Power-play dynamics. The current ecosystemseems harmonious, but banks, fintechs, orcolossal tech corporations may emerge as

EMBEDDED FINANCE
HAS GAINED ENOUGH
MOMENTUM TO
P L A C E I T AT T H E
FOREFRONT OF THE
NE X T TECHNOLOGICALRE VOLUTION

predominant players.

- Application programming interfaces(APIs). APIs enable embedded finance’s

The horizon of embedded finance, pushedstraightforward integrations. Expect afurther by AI, promises a world where financeproliferation of robust, secure, and intuitive

isn’t just a sector but an integral part ofplug-and-play APIs, anchoring flawless

our experiences, incorporated effortlessly

cross-platform functionalities.

into daily lives, decisions, and aspirations.

- Open banking. Embedded finance amplifies
open banking, accentuating collaborations,data sharing, and relationships between banks

Inclusivity, personalization, and unparalleleduser experiences will characterize the futureof embedded finance.

and third-party developers.

- Sectorial expansion. The ripple effectof embedded finance won’t be confinedto traditional sectors. The integration offinance will present myriad opportunities
in healthcare, real estate, and education.

THE INTERSECTION OF AI & FINANCIAL SERVICES

10ARTHUR D. LITTLE

VIEWPOINT

CONCLUSIONSHAPING THE FUTURE
OF FINANCE

AI COMPLEMENTS EMBEDDED FINANCE
AND AMPLIFIES ITS CORE STRENGTHS

Connecting AI and embedded finance is not just an enhancement
to accelerate growth — it’s a trend poised to redefine the contoursof digital financial services. AI complements embedded financeand amplifies its core strengths by:1

Promising a user-centric environment that emphasizes trust
and transparency

2

Contributing to value-focused personalized products and services

3

Automating processes to save time and minimize frustration

4

Building trust swiftly by prioritizing security and derailing fraud

5

Redefining financial inclusion and improving access to products
and services

6

Interpreting standards and adhering to regulatory frameworks

AI promises to make embedded finance faster, deeper, and moremeaningful. The fusion of these two forces heralds a future where
financial services are omnipresent, intuitive, and most importantly,
centered around the individual.

THE INTERSECTION OF AI & FINANCIAL SERVICES

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