With financial markets in a state of flux and the en�re world under lockdown, at this point, it's unclear how the FinTech industry and global financial landscape will look a�er the pandemic. Over the past few years, FinTech companies have moved out of specific use cases and have begun to operate at scale. While they only catered to the needs of specific demographics once, FinTech players have now started offering services across the financial services landscape. Despite the ongoing uncertainty caused by the global pandemic, the sector is experiencing an increase in demand for digital banking so�ware which could provide a necessary boost to FinTech companies especially at �mes when funding from other sources may not be available. Many governments across the world are also trying to mi�gate the impact of Covid-19 by encouraging more partnerships between tradi�onal banks and FinTech companies, most of which tend to be unaffected by tradi�onal opera�ons and processes, making them more efficient than their counterparts. (Source: globalbankingandfi-nance.com) The industry es�mates have predicted the financial services market to reach $26.5 trillion mark by 2022, growing at a rate of 6%. Asia-pacific has the largest financial services market, followed by North America. The global banking sector is in a be�er shape today than it was 10 years ago, a�er the recession. With the growing par�cipa�on of Millennials and Genera�on Z, the finance sector is expected to grow and shi� to mobile as well as online banking. This growth has driven a rise in startups and fintechs compe�ng for a share in business. Indeed, Fintech’s have received a total investment of over US$ 8 billion in past 22 quarters across 1031 deals (Source: Tracxn).

The Financial Services industry in India includes the capital markets, non-banking financial companies (NBFCs), and the insurance sector. In 2019, the gross na�onal savings as a percentage of Gross Domes�c Product in India stood at 30.50%. By the end of FY 2018, the ini�al public offerings amounted to around Rs. 84,357 Crore or USD 13,068 mn. In FY 2019, total funds raised stood at Rs.19,900 crore or USD 2.85 billion. Moreover, by 2024, the number of Ultra High Net Worth Individual (UHNWI) is es�mated to rise to 10,354 from 5,986 in 2019.

India has scored a perfect 10 in protec�ng shareholders' rights on the back of reforms implemented by the Securi�es and Exchange Board of India (SEBI) in World Bank's Ease of Doing Business 2020 report. (ibef.org)

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The Global Financial Industry

Indian Financial Services Industry Analysis

Financial Analy�csExploring the arsenal for risk modelling, transac�on, and customer diagnos�cs.

Funding activity across Fintech’s in India

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Conneqt Advantage

The Financial Services sector deals with an enormous amount of data. Here, smart analy�cs will act as a compe��ve differen�ator amongst the finance industry players. The true strategic value of big data streams can op�mize opera�onal decisions in core processes while predic�ng consequen�al outcomes at cri�cal decision points, such as:

• Growing revenues• Op�mizing costs and mi�ga�ng risks • Using advanced big data analy�cs to automate business processes • improving regulatory compliance, and • Enhancing customer experience

These capabili�es are enabled by analysing both structured and unstructured data within the data stores available to the banking and financial services companies. By combining advanced consul�ng exper�se with internal organiza�on commitment, you can greatly enhance the strategic value of data.

Financial Services Analy�cs

Domain Expertise: Our skilled and experienced professionals in the banking domain work across a range of banking segments. Our team possesses deep analy�cal knowledge and experience in loan modifica�on – fraud detec�on domain. We implement predic�ve analy�cs to offer delinquency models, propensity of mortgages, and mi�ga�on forecas�ng. We deploy automa�on in solu�ons where necessary.

Proven Track Record:To implement our core-banking systems with enterprise architecture in CRM and integra�ng solu�ons.

Transformational Solutions:We implement process reengineering and process ra�onaliza�on techniques/ models to reduce costs. We deploy flexible pla�orms with the ability to interact with a host of applica�ons.

Financial ins�tu�ons exist not just to provide a safe place to save money, but also to provide avenues where customers can invest their money and procure loans for their expenses. A healthy finance industry is a good sign of a strong na�onal economy. When money flows freely from and to consumers, it enriches all other industries.

Therefore, it is important to remember that financial ins�tu�ons need to operate at peak efficiency, ensuring great returns for themselves as well as their customers, to make sure that every other cog in the economy's machine runs smoothly.

Data analy�cs works by tapping into the rivers of data that financial ins�tu�ons are already collec�ng and storing, by adding new methods of data collec�on. Once all this data is collected, we can use computers to analyse and find pa�erns in data.

These pa�erns give us insights into customer habits, market trends, opera�onal inefficiencies, and bo�lenecks. Using these insights, we can find new methods and strategies to improve revenue, retain customers, and increase profits.

Overview of Analy�cs

Financial Risk Modeling

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In the past, risk modeling was a manual exercise, performed on a qualita�ve basis. Although this method does have some merits, it has the poten�al to miss some key details in few cases, falsely iden�fying risky investments as safe. It can even misiden�fy safe investments as risky.

Profitability

Customer Profitability

Customer Life Time Value

ChannelProfitability

Loca�onProfitability

ProductProfitability

Risk

Credit Risk

Fraud, AML

Liquidity Risk

Collec�ons And Credit Exposure

DefaultManagement

Campaign Analy�cs

Call Center Analy�cs

Cross Sell Analysis

Customer Acquisi�on

Customer Behavior Analysis

Customer Loyalty

Rela�onship Marke�ng

Asset & Liability Management

Capital Alloca�on Analysis

Liquidity Analysis

Credit Loss Provision

Net Interest Margin Variance

Funds Marturity Analysis

Structured Finance Analysis

FINANCIAL ANALYTICS

To gauge financial risk, we look at a customer's financial records, their banking history, credit history, and repayment history to find out whether he/she has enough loan repayment ability. Addi�onally, with a similar approach, one can gauge the risks in investment opportuni�es basis past performance, as well as exis�ng status.

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Business Need:

Approach:

In this scenario, the expected outcome is to categorize customers and investments into two primary buckets high-risk and low-risk. Based on the category, the financial ins�tu�on can choose at their discre�on whether to lend or invest.

Outcome:

The need to weigh financial risks is important since it can show which customers are eligible for loans without worrying about repayment. Addi�onally, it can also analyse investment opportuni�es, to gauge whether a par�cular investment is likely to yield be�er returns compared to another.

Business Need: By finding out risky investments and customers, financial ins�tu�ons can decide with concrete knowledge that some investments and lending are not worth the risk, thereby reducing losses incurred by the ins�tu�on.

Diagnos�c Analy�csDiagnos�c Analy�cs analyses data to answer the ques�on of what happened to augment decision support. understand the basics of what happened. This can be for a mutual fund that didn't yield promising returns or a set of customers who couldn't pay back their loans.

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By evalua�ng past repayments of customers, or financial records of companies seeking investment, diagnos�c analy�cs can produce a report on why a certain investment or loan has failed. This can be used for future lending ac�vi�es.

Approach:

The expected outcome of diagnos�c analy�cs is to have reports that provide solid informa�on on why a specific investment or lending failed, thereby helping financial ins�tu�ons in scru�nizing which investments to priori�ze and which ones to discard.

Outcome:

Business Need:

By analyzing customers, it is possible to dis�nguish high-value customers from high-risk customers. High-value customers can be offered more products to increase their customer rela�onship with the financial ins�tu�on, while high-risk customers can be denied or charged higher interest rates on loans.

Customer Analy�csNot all customers are equal. Some customers have higher spending poten�al, making them be�er and safer opportuni�es for loans. But other customers won't be as reliable when it comes to collec�on of loan repayments.

Diagnos�c Analy�cs

Detaileddashboards

Explanatorysta�s�calmodels

Enthusias�cconsump�onof finding/

insights

Implemen�ngrecommenda

�onscan be difficult

Causal / driveranalysis

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The algorithm will look at past repayments, credit scores, and outstanding dues, as well as the customers’ current economic situa�on to dis�nguish which category a customer belongs to.

Approach:

By crea�ng different buckets for customers, financial ins�tu�ons can offer more and be�er products to high-value customers to retain their patronage, as well as loyalty rewards. High-risk customers can be charged a higher interest rate to accommodate their higher risk or be denied loans en�rely.

Outcome:

By analysing the data between past transac�ons for assets that are delivered to buyers for investment and expenditure op�ons, we can gain insights into what buyers will buy, and what kind of loans are a�rac�ve to borrowers.

Approach:

By op�mizing asset lending and loan servicing, financial ins�tu�ons can expect to find greater revenue from their investment and loan por�olios.

Outcome:

Business Need: By u�lizing transac�on data analy�cs, financial ins�tu�ons can maximize the poten�al number of buyers and increase the purchase price of assets the financial ins�tu�on puts out.

Transac�on Data Analy�csTransac�on analy�cs uses data, technology, and advanced quan�ta�ve analysis to drive accurate observa�ons and insights. This is essen�ally the usage of knowledge and raw data to replace specula�on.

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The Process of Analy�cs Adop�onAdop�ng analy�cs into finance is not a one-size-fits-all solu�on. What works for one financial services company may not work for another. The magnitude, size, goals, and complexity of the implementa�on all factored into the equa�on.

Before implemen�ng analy�cs it is important to iden�fy a set of problems the solu�on will eventually be tailored around. These problems serve as a base skeleton discovering insights and thereby help in cra�ing the solu�ons. Some use cases should also be classified according to the severity or magnitude of impact. For instance, there can be a plethora of use cases in a retailer's working space, but some specific issues will create a greater impact than others. When working on a budget, it is important to ensure that companies focus on their biggest priori�es. A�er these use cases are set, the next step would be to measure the data and the impact of future solu�ons.

Identifying Internal Use Cases:

A�er iden�fying the changes required to be made, the next step is to measure the impact of those changes. This means asking a few key ques�ons, such as:

• What are the performance goals a�er deploying the solu�on? • How are these goals measured? • Does the organiza�on have necessary tools to measure them?

Measuring Analytics:

Companies must consider answering some ques�ons before adop�ng analy�cs, such as:

• Identifying internal use cases• Measuring Analytics • Finding Required Talent• Technical requirements

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This will provide valuable insights into finding out whether the deployed solu�on is performing as intended, or if it is causing any unintended side effects.

www.conneqtcorp.com | marke�ng@conneqtcorp.com | 70362-99999

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HyderabadCorporate Office & Opera�ons, 1-8-371, Gowra Trinity, S. P. Road, Chiran Fort Lane, Begumpet, Hyderabad-500016 Telengana, India.

We hope this gave you be�er insight into how Data Analy�cs can help your company reach new business goals. If you have any ques�ons, please contact us using the details below.

Thank You

Implementa�on of data analy�cs requires people with specialized skills to implement and effec�vely monitor the solu�on. Different BFSI players will have varied requirements in finding such talent. Some of the key ques�ons to be answered before hiring analy�cs resources are:

• Can the organiza�on afford to have an in-house team working on analy�cs? • Does the organiza�on need a permanent analy�cs team in-house all the �me? • Can the organiza�on use an external analy�cs firm to fulfil its needs?

On a final note, an organiza�on has to learn and define what are the technical requirements expected from an analy�cs prac�ce. Some financial ins�tu�ons are focused on preven�ng loan defaul�ng, while others want to broaden their investment por�olios. Different goals have different technical requirements, and companies seeking to employ analy�cs solu�ons must have a clear idea of what they need, what is already available, and what they addi�onally need to acquire.

Finding Required Talent:

Technical Requirements:

By answering these ques�ons, a company can understand what kind of analy�cs requirements they can employ.