Innovations in Banking - The emerging role for Technology and Al'

(Remarks delivered virtually by Shri M. Rajeshwar Rao, Deputy Governor, Reserve Bank of India
- December 22, 2023 - at the 106 Annual Conference of Indian Economic Association in Delhi)

Distinguished participants, Good Morning.

1. At the outset, let me thank the organisers for inviting me and giving this
opportunity to share a few thoughts on this very pertinent theme of
finance, banking and technology. 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.

2. The banks have played an extremely important role in supporting the
growth story of the Indian economy. If we were to analyse the evolution
of Indian banking sector over the last 5 decades, we can classify this
evolution in three distinct phases - post nationalisation, liberalisation, and

the third and current phase which we could term as democratisation.

3. 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.

1 Inputs provided by Chandan Kumar, Pradeep Kumar and Saurabh Pratap Singh are gratefully acknowledged.

Nationalisation Liberalisation Democratisation

=== Credit-to-GDP ratio === Investment-to-GDP ratio <== Deposit-to-GDP ratio === Credit-to-De posit ratio

Figure 1: Historical overview of Indian Banking
4. 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.

5. While the first two events (namely nationalisation and liberalisation) are
definitive in nature, /.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.

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2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2028 2021 2022

ss--AdjustedNon-FoodBankCredit = I = = - #eseee Net credit by Non-Banks including NBFCs, HFCs, AIFls
coccee Public issues by financial entities * © «© © Gross private placements by non-financial entities

eeccce Net issuance of CPs subscribed to by non-banks «°° Foreign borrowing (ECB, FCCB, Short term credit)

Figure 2: Flows of resources to commercial sector (% Lakh Cr)

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 %140 trillion. This is roughly 43% of total value of retail payment

transactions?.

9. 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 start-ups, fintech's and other
entities to test and experiment with new products in a controlled

environment.

10. 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.

11. 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 Al as it is commonly known, is also being cited as being in
the same league and proponents of Al 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.

2 https://www.npci.org.in/PDE/npci/statics/Retail-Payments-Statistics-Nov23.pdf

Therefore, let me share a few thoughts on the adoption of Al by the banking

and financial services sector and risks and rewards that this may entail.

12. As I understand, Al models can be placed in two categories - the first set
is the traditional Al models while the second is categorised as generative
Al or GenAl. The differentiation between the two is based on their
capabilities and applications. Most of the Al models currently available are
in the form of traditional Al and have been designed to perform a particular
task or set of tasks by responding to a set of inputs or instructions.
Traditional Al 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.

13. The development of GenAl - a type of Al 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 GenAl 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.

14. Current estimates on Al's boost to productivity and economic growth are
substantial but highly uncertain. Academic research suggests that workers
in early Al-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 Al could,
ceteris paribus, boost global GDP by about 7 percentage points over a 10-

year period.

3 https://www.gspublishing.com/content/research/en/reports/2023/10/30/2d567ebf-0e7d-4769-8f01-
7c62e894a779.html


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In the financial sector, we are seeing several banks and non-banks
experimenting with Al. 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 Al 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
Al 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 Al
could have a deep impact, on productivity, jobs and income distribution.
The advocates of Al 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 Al 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

18. Alis as good as a the data on which is 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.

19. Whereas traditional financial models are usually rules-based with explicit
fixed parameterisation, Al models drastically change the process as they
are able to learn the rules and alter model parameterisation iteratively.
This aspect makes many Al 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® which financial

institution need to be careful about while deploying Al models

4 A vulnerability where an attacker can inject and execute unauthorized code within an Al/ML model, potentially
leading to malicious actions and compromising system integrity.

5 The manipulation of training data with malicious intent to influence the performance of an Al/ML model,
causing it to make incorrect predictions or exhibit biased behaviour.

8 The phenomenon where the statistical properties of the input data for an Al/ML model change over time,
potentially causing a decline in performance as the model may become less accurate or reliable.

7 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.

8 The manifestation of unfair or discriminatory behaviour in an Al/ML model's predictions, influenced by biased
training data or the algorithm itself, leading to unequal treatment of different groups.

Governance

20. Al 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 ''t 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.

21. 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 Al driven systems. Financial institutions would therefore need to
institute governance structures that oversee the entire lifecycle of Al
systems, specially the GenAl - 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 Al

applications with extant laws and regulatory standards.
Transparency

22. The Al 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 Al
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

9 an attack involving the insertion of carefully crafted input prompts to manipulate the output of a language
model, leading to undesired or potentially harmful responses.

10 An Al/ML model's tendency to generate outputs that are not grounded in reality or data it has been trained
on, leading to the creation of false or nonsensical information.

I The generation of harmful, offensive, or inappropriate content by an Al/ML model, often reflecting the biases
present in its training data and posing risks in applications such as natural language generation or image
synthesis.

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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 Al.

In this context, let me outline ten aspects, which financial institutions
looking to deploy Al based models, may consider while designing Al
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:

Fairness: \t 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.

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.

Accuracy: The entities deploying Al should strive for accurate and
appropriate training data to minimize errors in decision making.
Identifying and understanding the types of errors the Al models make and

continuously work to minimize false positives and negatives is the key.

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
need to be consistent and too frequent changes to suit specific interests

need to be eschewed.

Data Privacy: In today's digitized world, adhering to data protection

regulations and ensuring that personal information is handled securely

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and responsibly is of utmost importance. Therefore, Al 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.

Explainability: The entities shall be able to provide clear explanations for
the factors influencing decisions or output to enhance transparency and
build trust in Al 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.

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 Al

model.

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.

Monitoring and Updating: Regularly monitoring the performance of Al
engines and updating them as may be required to adapt to changing
market conditions and emerging risks may have to be ensured. Also,
monitoring the evolution of self-learning algorithms is critical to ensure

that they continue to perform as envisaged originally.

Human Oversight: The entities should include human oversight to

address complex or ambiguous cases and to ensure that ethical

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considerations are taken into account. This would also ensure that any
unintended consequences and governance issues are detected in a

timely manner and addressed.

I think that incorporating these aspects would help in developing the public
trust if we truly want to exploit the transformative potential of Al. 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 Al.

In addition to the above, there are only a handful of entities globally which
have the large amount of data available to train GenAl 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 Al 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 Al
models are deployed in high-value decision-making use _ cases.
Development and deployment of Al models need close human supervision
commensurate with the risks that could materialize from employing the
technology by the financial institutions. As the adoption of Al is increasing,
global efforts to develop regulatory frameworks to help guide the use of Al
applications, are also increasing and greater cooperation in this process

would be required. Our collective endeavour should be to embrace this

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evolution with mindfulness and a sense of responsibility, while committing

to a future where technology serves as an enabler for the society at large.

KEKE

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