AI models drastically change the process as they are able to learn the rules and alter model parameterisation iteratively. This aspect makes many AI models a black box which are difficult to decode for audit and supervisory review.
FinTech BizNews Service
Mumbai, January 1, 2024: (Remarks delivered virtually by M. Rajeshwar Rao,
Deputy Governor, Reserve Bank of India – December 22, 2023 at the 106th Annual
Conference of Indian Economic Association in Delhi. Topic: Innovations in Banking -
The emerging role for Technology and AI. The text of the speech, released by the
RBI on 1-1-24):
In my address, I would largely focus on RBI’s efforts to foster innovation in financial
ecosystem and how the emerging technologies such as artificial intelligence are
likely to reshape the financial landscape.
The banks have played an extremely important role in supporting the growth story of
the Indian economy. If we were to analyze the evolution of Indian banking sector
over the last 5 decades, we can classify this evolution in three distinct phases – post
nationalization, liberalization, and the third and current phase which we could term
as democratization.
The level of financial intermediation at the time of independence was quite low in
India – ranging below 10 per cent of the GDP. This was, more or less, the case over
the next two decades or so. With limited financialisation and outreach of banking
services, the scope to mobilise deposits, facilitate credit flow, and support the
aspirations of a developing nation was constrained. But this trend changed with the
nationalisation of banks which ensured wider banking reach. This led to opening of
bank branches across the country resulting in greater mobilisation of deposits and
growth in credit, primarily for catering to the credit needs of priority sectors.
The second distinctive phase in the evolution of the banking sector was observed
during post-liberalisation phase (from 1991) till about 2003-04. During this period,
financial intermediation was fundamentally transformed as globalisation and
integration opened up various growth opportunities leading to increase in demand for
credit. Also, in tune with the spirit of economic reforms, private entities were allowed
to enter banking sector, and they supplemented the collective endeavour of banking
industry to support the growth story. Economic growth aided by institutional and
other attendant reforms along with greater technological adoption in the banking
sector spurred a robust growth in both deposit and credit during this period.
While the first two events (namely nationalisation and liberalisation) are definitive in
nature, i.e., we can attribute definite time stamps for these events, the third phase –
democratization of banking services, which we are currently experiencing, is
continuing as a gradual but transformative process, the pace of which has
accelerated over the last few years. This democratisation phase has coagulated of
late, underpinned by the trinity of financial inclusion, increase in financial literacy, and
focus on consumer protection. This phase has also witnessed massive expansion of
banking outreach, especially amongst the hitherto “excluded” sections through
innovative delivery models such as the use of banking correspondents. This
democratisation of financial services got a major push through Pradhan Mantri Jan
Dhan Yojana and direct benefit transfer (DBT) scheme along with proliferation of
mobile and telecom services. I would not be off the mark when I say that that this
phase has been a perfect combination of demand-pull and supply-push models
working in tandem. Today, we are witnessing the unfolding of true democratisation of
financial services where customers are greatly enabled to make informed choices
among the suit of available financial products offered by banks and other financial
service providers.
While we are discussing the role of banks in India’s economic journey, let’s not lose
sight of the contribution and the increasing importance of other financial
intermediaries. Although bank credit remains the dominant mode for meeting the
growing credit needs of commercial and household sectors, the share of non-bank
financial companies, micro-finance institutions and of late, market instruments has
also been increasing. Non-banks, through their innovative delivery and appraisal
methods, have further expanded the penetration of credit across the geographical
length and breadth of the country. They have, along with banks, contributed
immensely to this journey of democratisation of financial services.
India has made remarkable progress over the years to become the fifth largest
economy today. It is also poised to become the third largest economy over the next
few years and have the aspiration of becoming a developed nation by 2047. Reserve
Bank too has played a crucial role in unfolding of this growth story. We are one of the
very few central banks around the world which has been entrusted with a
developmental mandate along with traditional central banking functions.
For the next phase of development of our financial architecture to support the
aspirational growth of a rapidly growing economy, the RBI is creating world class
financial infrastructure. Let me cite a few examples. Today, India has one of the most
advanced state-of-the-art payments system that is affordable, accessible,
convenient, fast, safe and secure. Our payment infrastructure caters to the needs of
a diverse group of consumers and offers a wide array of options for executing all
types of transactions. This has led to a revolution in banking and financial services
making the banking truly ‘anytime, anywhere’. Everyone is aware of the success of
UPI and the convenience it offers. UPI symbolises the ultimate democratisation of a
financial product which is available to everyone including to customers who may not
have smartphones as well as for undertaking offline transactions. To put things in
perspective, during the FY 2022-23, UPI facilitated about 83 billion transactions with
approximate value of Rs140 trillion. This is roughly 43% of total value of retail
payment transactions.
Another technological initiative where the RBI has emerged as a leader is the
Central Bank Digital Currency (CBDC). We were one of the few major economies to
launch CBDCs when we launched pilot phases of wholesale and retail CBDC in
October and November 2022, respectively. Apart from its own initiatives, the RBI has
facilitated technological innovations in banking, non-banking, payment systems and
financial markets space. With its commitment to technology-led innovations, the RBI
has set-up the Reserve Bank Innovation Hub (RBIH) to accelerate innovation across
the spectrum of financial services. In addition, the RBI has also instituted a
regulatory sandbox to provide a platform for startups, fintech’s and other entities to
test and experiment with new products in a controlled environment.
Discussions about emerging technologies offers an opportunity to dwell a bit on how
the financial industry would need to interact with the newer technologies and the
breakthroughs in areas such as artificial intelligence.
At different points of time, there have been breakthroughs which have redefined the
future evolution of human societies such as the invention of the wheel, the steam
engine, development of vaccines, the computer, and more recently internet and
mobile phones. The emergence of Artificial Intelligence or AI as it is commonly
known, is also being cited as being in the same league and proponents of AI sound
convinced that it is going to transform the future. I am no expert on technology and
my focus is limited to understanding its implications for the economy and the
financial sector. Therefore, let me share a few thoughts on the adoption of AI by the
banking and financial services sector and risks and rewards that this may entail.
As I understand, AI models can be placed in two categories – the first set is the
traditional AI models while the second is categorised as generative AI or GenAI. The
differentiation between the two is based on their capabilities and applications. Most
of the AI models currently available are in the form of traditional AI and have been
designed to perform a particular task or set of tasks by responding to a set of inputs
or instructions. Traditional AI models can also learn and identify patterns from the
available data set and can make predictions based on the available data. However,
they can only respond within the pre-defined boundaries and follow specific pre-set
rules.
The development of GenAI – a type of AI technology that can produce various types
of content, including text, imagery, audio and synthetic data – has garnered a very
strong interest about its potential economic impact. It is said that the GenAI
possesses general intelligence and cognitive abilities comparable to those of a
human being. It is not confined to a specific set of tasks but can adapt and learn in
various domains, demonstrating a level of autonomy, reasoning, and problem-solving
capabilities.
Current estimates on AI’s boost to productivity and economic growth are substantial
but highly uncertain. Academic research suggests that workers in early AI-adopting
firms experience higher labour productivity growth; most estimates imply around a
2–3 percentage point increase per year. One estimate by Goldman Sachs suggests
that generative AI could, ceteris paribus, boost global GDP by about 7 percentage
points over a 10-year period.
In the financial sector, we are seeing several banks and non-banks experimenting
with AI. Global experience, so far, however, suggests that such deployment is mostly
limited to back-office work and optimisation of business processes to deliver
efficiency gains. Some of the banks have also deployed AI solution to manage
compliance requirements which are routine in nature, for identification of patterns in
transactions or payment to detect money laundering attempts or for facilitating cross-
border transactions and settlements. Some entities have also reported to deploy AI
solutions in customer facing processes such as for making lending decisions or
identification of target customer segments.
Given its transformative nature and potential, if realised, generative AI could have a
deep impact, on productivity, jobs and income distribution. The advocates of AI
expect widespread benefits for the economy and society, including increase in
income levels, automation of repetitive tasks and obtaining better insights by
combining different sets of information and data which may be otherwise difficult for
human processing. There are others who are more sceptical and point to several
societal consequences, including increased unemployment. They also point out that
if the long-term benefits are largely benign, the reallocation of resources and labour
in the transition could be challenging. We have also seen these concerns being
expressed in India with reference to IT sector, but the debate is ongoing and is
unlikely to be settled in near future.
Let me however flag a few concerns and also elucidate our expectations from the
financial institutions deploying AI in their business processes and decision making.
While some of these concerns are design specific risks such as biases and
robustness issues, others are more traditional and user specific such as data
privacy, cybersecurity, consumer protection and preserving financial stability. These
issues could be placed into three broad categories – data bias and robustness,
governance and transparency.
Data Bias and Robustness
AI is as good as a data on which it has been trained and thus inherits the issues,
biases and errors in its training data. Human beings accumulate such training data
over a lifetime of exposures, experiences, evidences, and upbringing. That makes us
capable of coming to different conclusion based on the same data set. Further,
humans can collaborate, combine and brainstorm to reach at an optimal solution.
This is not to say that humans are free of biases, but we have embedded checks and
balances in the institutional decision-making framework to check and prevent them.
Whereas traditional financial models are usually rules-based with explicit fixed
parameterisation, AI models drastically change the process as they are able to learn
the rules and alter model parameterisation iteratively. This aspect makes many AI
models a black box which are difficult to decode for audit and supervisory review.
Besides, there are several risks and vulnerabilities such as arbitrary code execution,
data poisoning, data drift, unexpected behaviour, and bias predictions.
A vulnerability where an attacker can inject and execute unauthorized code within an
AI/ML model, potentially leading to malicious actions and compromising system
integrity. The manipulation of training data with malicious intent to influence the
performance of an AI/ML model, causing it to make incorrect predictions or exhibit
biased behaviour.
The phenomenon where the statistical properties of the input data for an AI/ML
model change over time, potentially causing a decline in performance as the model
may become less accurate or reliable. An undesirable and unpredictable response or
output from an AI/ML model, often occurring in situations outside its training data
distribution or due to unanticipated inputs.
The manifestation of unfair or discriminatory behaviour in an AI/ML model's
predictions, influenced by biased training data or the algorithm itself, leading to
unequal treatment of different groups, which financial institution need to be careful
about while deploying AI models.
Governance
AI may also pose some novel challenges for governance, especially where the
technology is used to facilitate autonomous decision-making and may limit or even
potentially eliminate human judgement and oversight. Some of the data and model
issues such as prompt injection, hallucinations and toxic output can also have
implications for governance frameworks, especially in financial institutions. This may
necessitate that regulators and the management have a re-look at the frameworks
for consumer protection, cybersecurity and data privacy.
Some suggestions have been made to overcome governance issues including the
option of ‘putting a human in the loop’ to help build trust in the AI driven systems.
Financial institutions would therefore need to institute governance structures that
oversee the entire lifecycle of AI systems, specially the GenAI - from data acquisition
to model training and continuous evaluation. Regular audits and assessments would
also be essential to verify the fairness, accountability, and compliance of AI
applications with extant laws and regulatory standards.
Transparency
The AI models are inherently complex and opaque requiring extra caution to ensure
accountability. For this reason, financial institutions may find it difficult to explain an
adverse or biased decision outcome from an AI model to customer or supervisors.
The self-learning capability may make the model discriminatory and induce behavioural biases after some
time. For example, an algorithm may predict gender of potential target customer from
shopping history of a person or ethnicity from location data. How do institutions
overcome these challenges in a transparent manner is the key to widespread
adoption and use of AI.
In this context, let me outline ten aspects, which financial institutions looking to
deploy AI based models, may consider while designing AI solutions in order to strike
a balance between innovation and responsible use of technology; to ensure fairness,
prevent biases, and safeguard consumer privacy. These are:
i) Fairness: It should be ensured that the algorithm does not discriminate
against anyone based on attributes which are otherwise considered
unethical or prohibited by law. This can be achieved by conducting regular
fairness audits of the algorithms and outcomes including external
validation and by employing techniques to identify and rectify any
unintended biases.
ii) Transparency: All stakeholders should be aware about what are the
inputs and how the decisions are being arrived at. This should be achieved
by making the algorithmic decision-making process understandable and
explainable to both regulators and consumers.
iii) Accuracy: The entities deploying AI should strive for accurate and
appropriate training data to minimize errors in decision making. Identifying
and understanding the types of errors the AI models make and
continuously work to minimize false positives and negatives is the key.
iv) Consistency: The entities should ensure consistent application of the
algorithm across different situations to avoid biases or unfair advantages
and to ensure equitable outcomes. Parameters entering the models also
11 need to be consistent and too frequent changes to suit specific interests
need to be eschewed.
v) Data Privacy: In today’s digitized world, adhering to data protection
regulations and ensuring that personal information is handled securely and
responsibly is of utmost importance. Therefore, AI model should be
designed to adhere to data protection protocols and regulations and
entities should ensure that personal information is always handled
securely and responsibly.
vi) Explainability: The entities shall be able to provide clear explanations
for the factors influencing decisions or output to enhance transparency and
build trust in AI models. Clear understanding of the inputs, processes and
output by the entities and establishing channels for redressal of customer
queries or disputes will help in promoting trust.
vii) Accountability: Clear lines of accountability for the outcomes of
algorithmic decisions shall be ensured by making it clear who is
responsible for the performance, robustness and fairness of the model.
Entities should implement a comprehensive governance framework that
includes regular audits, internal reviews, and external assessments to hold
individuals responsible for addressing any issues related to the AI model.
viii) Robustness: The entities must undertake rigorous validation and
testing to ensure that the algorithm performs well under different
conditions and is not overly sensitive to minor changes in input data.
Regularly updating the model's training data to include a broad spectrum
of conditions, ensuring adaptability to changes in the economic landscape
and maintaining robust performance over time is also critical.
ix) Monitoring and Updating: Regularly monitoring the performance of AI
engines and updating them as may be required to adapt to changing
market conditions and emerging risks may have to be ensured. Also, 12
monitoring the evolution of self-learning algorithms is critical to ensure that
they continue to perform as envisaged originally.
x) Human Oversight: The entities should include human oversight to
address complex or ambiguous cases and to ensure that ethical
considerations are taken into account. This would also ensure that any
unintended consequences and governance issues are detected in a timely
manner and addressed. 24. I think that incorporating these aspects would
help in developing the public trust if we truly want to exploit the
transformative potential of AI. Let me also point out that in addition to
institution specific challenges, there are several geopolitical and systemic
issues which would also engage us going forward. For example, like any
other technological development of the past, the access of technology is
going to be uneven among countries. Advanced economies (AEs) may
stand to benefit more than emerging market economies (EMEs) due to the
fact that EMEs’ have higher share of employment in sectors such as
agriculture and construction which would inherently have less
opportunities for application of AI.
In addition to the above, there are only a handful of entities globally which have
the large amount of data available to train GenAI models. This could give rise to
the questions of market power, competition and cross jurisdictional issues.
To conclude, let me say that as our banking sector evolves, emerging
technologies and AI will play a significant role in the process. We need to ensure
a supportive regulatory framework to harness its benefits while being mindful of
any potential adverse impacts and therefore robust governance arrangements
and clear accountability frameworks are important when AI models are deployed
in high-value decision-making use cases. Development and deployment of AI
models need close human supervision commensurate with the risks that could
materialize from employing the technology by the financial institutions.
As the adoption of AI is increasing, global efforts to develop regulatory
frameworks to help guide the use of AI applications, are also increasing and
greater cooperation in this process would be required. Our collective endeavour
should be to embrace this evolution with mindfulness and a sense of
responsibility, while committing to a future where technology serves as an
enabler for the society at large.
