Artificial Intelligence

Zenith Special Interest Group

Executive Primer

TRL

Artificial Intelligence

3 Types of

Artificial Intelligence:

Weak AI

▪Limited by programming –

won’t develop new skills

▪Analyses preferences and

improves over time

Strong AI

▪Learns new skills through

contextualisation

▪Applies knowledge to plan

ahead

▪Can adapt as changes occur

Superintelligence

▪Self-aware

▪Surpasses human

intelligence

▪Only exists in science

fiction

Artificial Intelligence is revolutionizing the financial

technology (fintech) industry, offering unprecedented

opportunities and challenges alike. As a rapidly advancing

branch of computer science, AI enables the simulation of

human thought and language through code, with recent

breakthroughs in machine learning and deep learning

propelling the field's growth.

For fintech companies, embracing AI presents a pathway to

stay competitive amid the increasing investments in AI

innovation by big tech firms. However, alongside these

opportunities, several critical challenges arise, demanding

careful attention.

In this executive briefing, we will explore the key challenges facing the fintech industry as it harnesses the

potential of AI. Specifically, we will examine the issues of bias in AI systems, the ethical implications of AI

adoption, and the importance of addressing security and maintenance concerns for AI systems.

By addressing these challenges, fintech companies can pave the way for widespread and responsible

adoption of AI tools, ensuring a safe, secure, and sustainable transformation of the financial services

industry.

In the following sections, we will delve deeper into each challenge, highlighting the Zenith program's

opportunities to assist fintech and financial services firms in overcoming these hurdles and unlocking the full

potential of AI technology. In our appendices, we will brief you on the landscape of each underlying

technology.

Biases in AI Systems

Key Highlights

▪AI algorithms can

inherit biases from the

data they are trained

on, leading to unfair

and discriminatory

outcomes.

▪Identifying and

mitigating bias in AI

systems is crucial to

building trust and

ensuring ethical AI

adoption.

▪Biases in AI can have

significant

consequences in

sensitive domains such

as finance, impacting

customer satisfaction

and regulatory

compliance.

Artificial Intelligence (AI) systems are prone to inheriting biases present in the data used for their training.

Technologies such as Data Annotation and Data Quality & Observability play vital roles in identifying potential

biases and ensuring that training data is diverse and representative. By incorporating these technologies, fintech

companies can reduce the risk of biased AI outcomes and promote fairness in their algorithms.

By adopting Synthetic Data generating practices, fintech companies

can broaden the training data sets to include more diverse

demographics in their customer data to improve the quality of the data

in.

To ensure ethical AI adoption, fintech companies should also invest in

technologies that relate to Model Validation & Monitoring. These

technologies help in understanding how AI models arrive at decisions,

making the decision-making process more transparent and

interpretable for customers and regulators.

To tackle biases effectively, fintech firms should adopt strategies such as diverse and representative data

collection, regular audits, and fairness-aware machine learning algorithms. Transparency in AI decision-making

can also aid in identifying potential biases and gaining customer trust.

Ethical Implications of AI Adoption

Key Highlights

▪Ethical considerations

in AI adoption involve

privacy, transparency,

fairness, and

accountability.

▪Fintech companies

must balance AI-driven

innovation with

societal and ethical

responsibilities.

▪Responsible AI

practices enhance

brand reputation and

foster long-term

customer loyalty.

As fintech companies embrace AI-driven innovation, they must navigate the ethical implications associated

with its adoption. Ethical considerations involve ensuring customer privacy, maintaining transparency in AI

decision-making, promoting fairness in algorithms, and being accountable for AI-driven outcomes.

To address ethical concerns related to data privacy, fintech companies should implement

Data De-Identification techniques. These technologies protect sensitive customer information while still

allowing AI models to derive valuable insights.

Transparency and explain-ability are vital to address ethical concerns in AI adoption. Consumers and

regulators are increasingly demanding explanations for AI-driven decisions. To enhance transparency, fintech

firms should adopt Interpretable AI models and establish clear communication channels to explain the logic

behind AI recommendations.

Fintech companies should acknowledge their responsibility for AI-driven

outcomes and establish mechanisms to rectify errors and address

customer complaints. Being accountable for AI systems' behaviour

fosters trust and loyalty among customers and stakeholders.

Responsible AI practices not only align with societal values but also

enhance the reputation of fintech companies. Customers are more likely

to engage with companies that demonstrate a commitment to ethical AI

practices, leading to long-term customer loyalty and sustainable growth.

Interpretable AI Explainable AI

Small models that

are inherently

interpretable

e.g. Small decision

trees or linear

models with a small

number of input

variables

The process of

applying a method

that models the

output of a more

complex model

This is performed

after model training

has been

completed

Security & Maintenance Concerns

of Artificial Intelligence

Key Highlights

▪The integration of AI in

fintech increases

cybersecurity risks and

potential

vulnerabilities.

▪Continuous monitoring

and updates are

essential to ensure the

robustness and

reliability of AI

systems.

▪Investing in AI security

and maintenance is a

proactive approach to

safeguarding financial

data and operations.

While AI offers transformative opportunities for fintech, it also introduces new cybersecurity risks and

vulnerabilities. The integration of AI in financial services creates potential entry points for cyber threats,

necessitating a proactive approach to security and maintenance.

Technologies like Resource Optimisation and Machine Learning Deployment are essential in enhancing AI

system robustness. These technologies ensure that AI models are efficiently deployed and managed,

minimizing potential vulnerabilities and optimizing real-time resource usage.

AI can also play a pivotal role in fraud

prevention and risk mitigation within fintech.

Implementing AI-driven Fraud Detection

systems enhances the security of financial

transactions and improves overall risk

management.

Considering the rapid evolution of cyber

threats, fintech companies must stay informed

about emerging security challenges and invest

in state-of-the-art AI security technologies.

Collaborating with industry experts and

sharing insights on security best practices can

further fortify fintech systems against

potential threats.

Zenith Opportunities

Benefits of Exploration

Through Zenith

1. Common frameworks for

adoption can be

crowdsourced

2. Reduce internal expertise

limitations through

community engagement

3. Time-to-market of

exploration in innovation

labs reduced

4. Reserve resources for

high-value development

5. Interoperability improves

adoption across the

industry

6. Keep in step with

security, standards, and

new feature development

7. Benefit from community

contributions across key

themes

8. Empower, retain, and

attract innovators and

developers

Identified

AI Themes

Identified

Blockers

In the Appendix, we have outlined 12 key themes that serve as innovation verticals

within the Zenith program.

These themes encompass cutting-edge technologies and trends in Artificial

Intelligence.

Embracing these themes will drive transformative advancements in fintech, fostering

collaboration, and collective innovation within our foundation.

We have identified 26 crucial blockers impeding the widespread adoption of AI

technologies in the fintech industry. These will be posted on the Zenith site and

repository.

To address these challenges, we call upon the wider development community to

propose open source solutions through the Zenith program.

Together, we can leverage the power of collective expertise to overcome these

obstacles and shape the future of AI-powered fintech solutions.

Join us in our mission to create secure, transparent, and ethical AI applications that

redefine the financial services landscape.

Appendices

Data Sheets

▪AI Chipsets

▪AI-Driven Fraud

Detection

▪Computer Vision

▪Data Annotation

▪Data De-Identification

▪Data Quality &

Observability

▪Generative AI

▪Machine Learning

Platforms & Deployment

▪Model Validation &

Monitoring

▪Natural Language

Processing

▪Resource Optimisation

▪Synthetic Data

▪Version Control &

Experiment Tracking

This report uses Technology Readiness Levels. For more information as to this

classification system, please refer to https://zenith.finos.org/docs/roadmap/TRL 1

456

AI Chipsets

Specialized processors

designed to accelerate AI

computations, enabling faster

and more efficient AI model

training and inference.

In fintech, AI chipsets drive

groundbreaking

advancements, powering

complex algorithms for fraud

detection, risk assessment,

and personalized financial

recommendations.

The high-performance

computing capabilities of AI

chipsets empower fintech

companies to deliver real-

time, data-intensive services,

transforming the way

financial institutions operate

and serve their customers.

You can find out more about

this subject in our AI Chipset

Primer on the Zenith GitHub.

Surging Demand

for applications

Deep learning

advancements

Need for

fast/efficient data

processing

Regulatory pressure to

improve fraud detection

Cost savings through AI-

driven automation

Integration across multiple

processes

AI-powered data analytics

Desire to enhance financial

inclusivity & accessibility

Legacy hardware

manufacturers

Traditional financial services

requiring adaptation

Skill shifts in job market as AI

impacts workforce

Faster & more accurate decision making

Risk mgmt. & fraud detection capabilities

Real-time data processing for insights

Optimised AI model training on chipsets

AI-powers Fintech expands market reach

Increased investment for innovative startups

Data storage and integration chipsets

Collaboration with data/chipset manufacturers

Disruption to trad. hardware supply chains

Success of

accelerated AI

model training

Scalability of AI

chipsets for high

volume data

Need for continuous upskilling to ensure

workforce proficiency

Growing ecosystem

of service providers

Availability of dev

tools & frameworks

Need for continuous upskilling to ensure

chipsets are implemented correctly

Data privacy

concerns

Integration

challenges with

legacy hardware

Regulatory compliance complexities

Short term

Initial adoption and

integration of chipsets

Medium term

Widespread deployment

in financial services

Long term

Evolution of chipsets for

autonomous solutions

Potential delays in

advancements and

innovations

Market resistance and

scepticism in AI-decision

making

Ethical considerations

surrounding responsible

use

AI chipsets offer a compelling opportunity for fintech, revolutionizing financial services through accelerated AI computations. Advancements in specialized hardware enable groundbreaking

applications in fraud detection, risk assessment, and personalized financial recommendations. The high-performance computing capabilities of AI chipsets empower real-time, data-intensive

financial services, transforming traditional banking and ushering in a new era of customer-centric financial experiences. Despite technical feasibility and potential financial benefits, challenges

like data privacy, regulatory compliance, and integration frictions require careful navigation. With a promising future outlook, early adoption and collaboration between fintech players and AI

chipset manufacturers will play a crucial role in shaping the trajectory of AI-driven fintech innovation.

AI-Driven

Fraud Detection

This leverages advanced

machine learning algorithms

to detect and prevent

fraudulent activities in real-

time.

In fintech, this technology

acts as a vigilant security

layer, continuously analysing

vast volumes of financial data

to identify suspicious

patterns and transactions.

By swiftly detecting and

mitigating fraud, AI-driven

systems protect financial

assets, preserve customer

trust, and enhance overall

cybersecurity in the rapidly

evolving digital financial

landscape.

You can find out more about

this subject in our AI Chipset

Primer on the Zenith GitHub.

Escalating cyber

threats

AI advancements

increase accuracy

& efficiency

Growing adoption

of digital financial

services

Regulatory mandates &

compliance reqs.

Significant cost

saving through

loss prevention

Brand reputation

& Consumer trust

Widespread adoption leads

to collective intelligence

Collaborative Industry-wide

defence strategy

Increased trust in FS

promotes further adoption

Cybercriminals facing more

evolved systems

Legacy fraud detection

losing competitiveness

Shift in the dynamics of

financial crime investigation

toward automation

Superior fraud detection & reduced losses

Enhanced customer trust & loyalty

Real-time response capabilities to incidents

Cost savings & less post-fraud investigations

Customer retention through reputation

Investments & partnerships for potential

applications following successful tests

Integration of AI-driven solutions to core infra

Collaboration between AI solution providers,

data aggregators & financial entities

Potential changes in risk assessment and

underwriting of Financial Services

Demonstrable

success in real world

scenarios

Scalability of AI

models to process

vast volumes of data

Continuous improvement and refinement of ML

models to adapt to evolving threat landscape

Readily available

dev. frameworks

Growing ecosystem

of talent working

with AI tools

Need for continuous upskilling to keep up with

emerging fraud techniques

Data privacy

concerns

Over-reliance on AI

models leading to

blind spots

Regulatory compliance complexities in

explainability of models

Short term

Integration efforts into

security infrastructure

Medium term

Industry-wide adoption &

increased reliance

Long term

Evolution of AI-driven

responses to emerging

fraud techniques

False negatives where AI

fails to detect new or

adaptive fraud patterns

Adversarial attacks

targeting AI models to

manipulate outcomes

Potential overfitting or

bias in AI models

affecting accuracy and/or

fairness of detection

AI-driven fraud detection represents a significant opportunity in fintech, providing real-time protection against sophisticated cyber threats. Advanced machine learning algorithms analyse vast

financial data to swiftly identify and prevent fraudulent activities, safeguarding financial assets and customer trust. Collaboration between fintech companies, financial institutions, and

cybersecurity experts is driving the development and adoption of robust fraud prevention solutions. The technology's impact is far-reaching, with macro network effects and improved

cybersecurity across the digital financial landscape. While the potential for financial benefits and competitive advantages is substantial, the implementation of AI-driven fraud detection requires

careful consideration of technical feasibility, frictions, and risks. By striking the right balance between innovation and responsible use, AI-driven fraud detection will continue to transform the

way financial entities combat financial crime, contributing to a more secure and trusted financial ecosystem.

Computer Vision

An AI technology that

enables machines to

interpret and understand

visual information.

In fintech, computer vision

revolutionizes various

processes, from automating

document verification and

identity recognition to

analysing financial charts and

visualizing data patterns.

By harnessing the power of

computer vision, fintech

companies streamline

operations, enhance user

experiences, and unlock

valuable insights from visual

data, driving efficiency and

innovation.

You can find out more about

this subject in our AI Chipset

Primer on the Zenith GitHub.

Increasing

availability of HQ

visual data

Growing demand

for automation &

efficiency

Rising interest in

data-driven FS

decision making

Competitive advantage

through visualising

complex data

Potential

improvements to

fraud detection

Need for fast &

accurate ID & docs

verification

More focus on visual

interaction with data

Combination of computer

vision with other AI tech

Improved accessibility &

inclusivity via automation

Obsolescence creates need

to adapt to automation

Conventional data analysis

methods challenged

Shift in the skill set required

within FS Operational tasks

Quick & seamless document processing

Enhanced security through adv. verification

Data-driven decision making for strategy

Automation of labour intensive doc verification

Higher customer engagement & satisfaction

Demonstration of technological leadership &

innovation of products with customer focus

Integration of end-to-end operations

Collaboration required to meet specific

industry needs

Streamlining of data collection & analysis

Real-world demos of

visual data

interpretation

Scalability to handle

large-scale real-time

data

Continuous improvement and refinement

required to improve accuracy & efficiency

Readily available

libraries, APIs and

tools

Growing ecosystem

of collaborating

entities

Need for continuous upskilling to keep up with

new data use cases

Data privacy

concerns over

processing

Ethical

considerations when

decision making

Potential biases affecting financial data analysis

& decision outcomes

Short term

Document verification

and basic data analysis

Medium term

Widespread POCs for

other FS operations

Long term

Augmented Reality based

financial interactions

Technical challenges

affecting decision making

Regulatory compliance in

ID verification &

customer data processing

Market resistance to

automated interpretation

of visual data

Computer vision technology holds immense potential in transforming fintech processes, enabling machines to interpret and understand visual information. Fintech companies are leveraging

computer vision to automate document verification, enhance fraud detection, and gain valuable insights from complex financial data. The technology's impact extends to improved user

experiences, data-driven decision-making, and increased efficiency in financial operations. While the opportunity is significant, challenges related to technical feasibility, ethical considerations,

and regulatory compliance must be addressed. The future outlook for computer vision in fintech is promising, with a gradual timeline for adoption and a need for careful risk management and

responsible implementation. By capitalizing on the benefits and addressing frictions, computer vision will play a pivotal role in driving innovation and efficiency in the evolving landscape of

fintech services.