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................BM-XCMYK
MUMBAI
BusinessLineWEDNESDAY • JANUARY 1 • 2020 5BANKING
OUR BUREAU
Mumbai, December 31
The banking system’slacklustre credit growth hasmore to do with the lack ofcredit demand rather thancredit availability, accordingto India Ratings and Research(IndRa). The weak credit off��take is likely to exacerbate thecurrent slowdown in terms ofintensity and recovery time, itadded.
In this regard, the creditagency underscored that corporate credit off��take frombanks will remain limited atleast in the near term, considering the subdued demand.
Risk averseness“The muted demand for loanis largely explained by the absence of capital expenditure(capex). Second, with the in
creasing risk averseness inthe system, credit is madeavailable to borrowers withstrong credit metrics and alsoat a reasonable price. However, the need for credit bythose entities is relatively less,either because of their strongbalance sheet or other alternative sources (External Commercial Borrowing, ForeignCurrency Convertible Bondsor domestic bond market),”said Jinesh Rajpara, analyst,in the research report.
The agency believes thatunlike in the past, the banking system is wellcapitalisedwith both private and publicsector banks ready to pushcredit growth.
Specifi��cally on the publicbanks’ side, while capitalavailability is no longer achallenge, the ongoing amal
gamation will keep the focusof the banks involved awayfrom credit growth in thenear term.
“On an overall basis, a weakeconomic sentiment resulting in lack of new project announcements, along with thesharp slowdown seen in thenonbanking fi��nance company (NBFC) sector resultingfrom the ongoing challengeson liquidity, are likely to keepthe credit growth expectations muted for the bankingsector in the near term,” thereport said.
Non-food credit growthOverall nonfood creditgrowth for scheduled commercial banks remainedweak in October 2019 at 8.3per cent yearonyear (yoy),down sharply from 13.4 percent yoy in October 2018, andmarginally up from 8.1 percent yoy in September 2019.At a more granular level,
growth in the retail segmentremained strong at 17.2 percent yoy (September 2019:16.6 per cent yoy; October2018: 16.8 per cent yoy).
Further, growth in retailloans contributed nearly 53per cent to the incrementalgrowth in scheduled commercial banks’ credit duringOctober 2019, the agency said
Annual credit growth inthe agriculture and industrysectors remained largelystable at 7.1 per cent and 3.4per cent, respectively, duringthe month.
However, the weakness incredit growth has intensifi��ed
in the services segment at 6.5per cent in October 2019 from27.4 per cent in October 2018.
Funding slowdownOn a subsector basis withinthe services segment, theagency found that funding tokey contributing sectors suchas nonbanking fi��nance companies (NBFCs) and other services had seen a sharp slowdown in October 2019 whencompared to October 2018.
IndRa assessed that NBFCsthat contribute about 30 percent to the services segmentsaw a credit growth slowdown at 26.8 per cent in October 2019 from 55.6 per cent inOctober 2018.
Other services segment,with a contribution of about23 per cent to the services segment, saw a credit growthslowdown at 12.7 per cent yoy in October 2019 from 35.6per cent yoy growth in October 2018.
Low credit offtake likely to worseneconomic slowdown, says IndRa Bank amalgamation will take focus away
from credit growth in the near termWX
Weak economic
sentiment, along with
the sharp slowdown
seen in the NBFC sector,
will keep credit growth
expectations muted
for the banking sector
SHOBHA ROY
Kolkata, December 31
The general insurance
industry in India, which
witnessed an overall
deterioration in financial
health this year on account of
several natural calamities
leading to higher
claims, will work to
strengthen the
balance sheet to
deliver better
customer service.
There is also likely to be a
massive transformation in the
insurance sector, which is set
to move from a push-based
industry to a pull-based one
in the next two to three
years.
In an exclusive interaction
with BusinessLine, Tapan
Singhel, MD and CEO, Bajaj
Allianz General Insurance
Company, spoke about the
impact of the various changes
in the general insurance
industry and the way forward.
Excerpts:
What were some of the
key challenges faced by
the general insurance
industry in 2019? How
do you see the next
year panning
out for the industry?
One of the biggest challenges faced was the fi��vefl��oods during the course ofthe year. As a general insurance industry, this directlyimpacts us and aff��ects almost all portfolios, including health, auto, and crop,among others.
Since the pricing of the industry is based on past experiences, prediction of suchmassive events in one year
would not have been takeninto consideration by insurance companies, thereby impacting the balance sheet.
However, global weatherchange is something we cannot brush under the carpetas something small and
insignifi��cant.This is one of the
biggest learnings (forthe industry) and it ishere to stay. Weather
change will play a big role inbusinesses and they willhave to align themselvesthinking about it, and thegovernment should alsothink about cities and townplanning.
How do you see the
overall health of the
general insurance
industry?
The overall results of the industry are expected to deteriorate when compared tolast year due to thesemassive events. The conditions are tough for mostcompanies because of theirsolvency situation due tosuch results.
However, this will lead toan interesting scenariowhere companies will have
to look at how they take climate change as an inherentrisk, and how they look atbuilding their portfolios,businesses and pricing.
Moving forward, how
will digital technology
impact the general
insurance industry?
What we called digital is nolonger a luxury or boardroom conversations now.The hyper speed of the internet, the kind of solutionsthat are emerging, and witha number of startups thinking of ways to look at businesses, this has changed.
Technologies such asblockchain, Artifi��cial Intelligence and Machine Learning, which we spoke abouttwo to three years back assomething that will happen,is already there; so, nowthere is a massive shift happening in terms of processes, business solutions,and how data is being collected and looked at.
For the insurance industry to be away from
this wouldnot
makeany
sense, and that is why mostcompanies are looking tofi��nd good solutions usingthese tools that benefi��t customer experience or getsome new products thatwere not there.
Customers who have gotused to an era of ecommerce are looking for instant delivery of productsand services. If you look atinsurance companies, theyare very traditional in theway they think.
So, it is not only about micro changes now; my beliefis that in the years to comeand starting from next year,you will see eff��orts beingmade by the industry tomake massive transformations to bridge all the gaps.
You said the insurance
sector will move from
being a push industry to
a pull industry. Can you
elaborate a little more?
The insurance industry hasalways been a push industrybecause nobody gets up tobuy insurance on his own,but when a conversationhappens, the person getsconvinced to buy it. That iswhy insurance companiesare still largely distributorled companies because conversations have to happen.
Now, this is where themassive transformation willhappen. Once a customerfeels that his problems orworries are getting solved,there will be full eff��ort interms of buying the product.So, from a push, we willmove to pull, and as a company, we are very keen tolead this transformation.
‘Insurance sector set to move froma push industry to pullbased one’
ZYCompanies will have to
look at how they take
climate change as an
inherent risk, and
how they look at building
their portfolios,
businesses and
pricing
TAPAN SINGHELMD and CEO, Bajaj Allianz General InsuranceCompany
YZ
O
OUR BUREAU
Mumbai, December 31
Corporates in the large segment are liquidityrich and,thus, could have limited liquidity requirements, according to the Reserve Bank of India’s latest Financial StabilityReport (FSR).
The report cautioned thatthis has implications for reviving the investment cycle, giventheir signifi��cant share inwholesale credit.
The RBI made the assessment after comparing thecharacteristics of the balancesheets of two categories of corporates – very large (aggregatedebt above ₹��5,000 crore) andlarge (aggregate debt between
₹��100 crore and ₹��5,000 crore). In terms of the fi��nancial
leverage metric, large corporates have steadily deleveraged.With regard to corporates’ balance sheet liquidity in boththese cohorts, clearly largecorporates were liquidityrich,with cash and marketable securities exceeding 40 per centof onbalance sheet debt ineach of the last four years, thereport noted.
Credit growth in wholesaleaccounts (aggregate exposureof ₹��5 crore and above) in thepast two years was dominatedby very large accounts (aggregate exposure above₹��5,000 crore), the report said.
The share of very large
credit (greater than or equal to₹��5,000 crore) in scheduledcommercial banks (SCBs)wholesale portfolio moved upfrom 30.7 per cent in March2017 to 33.3 per cent in March2018 to 38.7 per cent in March2019.
The share of large credit(₹��100 crore to ₹��5,000 crore)declined from 48.8 per cent inMarch 2017 to 46.2 per cent in
March 2018 to 42 per cent inMarch 2019. The share of medium credit (₹��25 crore to ₹��100crore) moved down from 9.8per cent in March 2017 to 9.7per cent in March 2018 to 8.9per cent in March 2019.
The share of small credit (₹��5crore to ₹��25 crore) moved upfrom 10.7 per cent in March2017 to 10.9 per cent in March2018, but declined to 10.4 percent in March 2019.
Financial firms dominate A broad split between fi��nancial and nonfi��nancial fi��rmsshows that credit growth in201819 was dominated by fi��nancial fi��rms (nonbanking fi��nancial companies). The disag
gregated credit growth of verylarge borrowers in the fi��nancial sector jumped 47.5 percent yearonyear (yoy) inMarch 2019 against 22.4 percent in March 2018.
Within the very large fi��nancial fi��rms category, creditgrowth for public sector undertakings (PSUs) soared 92.9per cent yoy in March 2019against 45.6 per cent in March2018. Credit growth for theprivate sector rose 39.5 percent yoy against 19.1 per cent.
The disaggregated creditgrowth of very large borrowers in the nonfi��nancial sectorrose 19.7 per cent yoy inMarch 2019 against 12.2 percent in March 2018.
‘Low fund demand by cashrich fi��rms can hit investment cycle’FINANCIAL STABILITY REPORT
OUR BUREAU
Mumbai, December 31
India’s Current Account Defi��cit(CAD) narrowed sharply to 0.9per cent of GDP in the JulySeptember (Q2) of FY201920from 2.9 per cent in the yearago period and 2 per cent inthe preceding quarter. Thiscontraction in CAD wasprimarily on account of lowertrade defi��cit.
CAD occurs when the valueof goods and services imported exceeds the value of exports. A large CAD can causethe domestic currency todepreciate.
In absolute terms, the CADin the reporting quarter (Q2)
came down to $6.3 billion from$19 billion in the yearagoquarter and $14.2 billion in thepreceding (AprilJune 2019)quarter. Trade defi��cit in the reporting quarter was lower at$38.1 billion when compared to$50 billion a year ago. Net services receipts increased by 0.9per cent on a yearonyear (yoy) basis on the back of a rise innet earnings from computer,travel and fi��nancial services,the RBI said.
Private transfer receipts,mainly representing remittances by Indians employedoverseas, rose to $21.9 billion,increasing by 5.2 per cent fromtheir level a year ago.
CAD narrows sharplyto 0.9% of GDP in Q2
OUR BUREAU
New Delhi, December 31
The Enforcement Directorate(ED) has attached assetsworth ₹��127.74 crore of immovable properties belonging toPixion Media and its groupcompanies in a bank fraudcase that runs into ₹��2,600crore.
The attached assets consist
of two commercial plots andnine commercial fl��oors ofgroup companies situated inMumbai, Chennai, Noida, andKolkata, according to a pressstatement issued by the ED.
The ED initiated the investigation under the Preventionof Money Laundering Act(PMLA) on the basis of sevenFIRs and chargesheets fi��led by
the CBI. Pearl Media, MahuaaMedia, Pixion Vision, PearlStudio, Pearl Vision and Century Communication are being investigated.
Investigation revealed thatthe accused – PK Tewari,Anand Tewari and AbhishiekTewari, directors of PixionGroup of companies – hadfraudulently availed loans to
the tune of ₹��2,600 crore fromvarious banks. The accusedpersons further diverted theloan amounts through bankaccounts of various companies and entities controlled bythem, which were fi��nally utilised in the procurement of assets at various places such asMumbai, Chennai, Noida, andKolkata.
ED attaches ₹��127crore assets of Pixion MediaBANK FRAUD
OUR BUREAU
Mumbai, December 31
To facilitate professional management and focussed attention to their bankingrelatedactivities, the Reserve Bank ofIndia (RBI), on Tuesday, directedUrban Cooperative Banks(UCBs) with deposit size of ₹��100crore and above to constitute aBoard of Management (BoM).
In its guidelines on constituting BoM in UCBs (other thanSalary Earners’ Banks), the central bank said it will be mandatory for such banks to constituteBoM for seeking approval to expand their area of operationand/or open new branches.These UCBs will also requireprior approval of the RBI for appointment of their CEOs.
UCBs with a deposit size lessthan ₹��100 crore and SalaryEarners’ Banks, however, are exempted from constitutingBoM, although they are encouraged to do so voluntarily. TheBoM will have minimum fi��vemembers and maximum 12.The CEO would be a nonvotingmember. A member of BoM canbe appointed in more than one
bank, subject to a maximum ofthree, provided that there is nooverlapping in area of operation. The members of BoMshould at all times satisfy the‘Fit and Proper’ criteria.
As per the guidelines, the RBIhas the powers to remove anymember of BoM and/ or theCEO if the person is found to benot meeting the criteria prescribed by it or acting in a manner detrimental to the interestsof the bank or its depositors orboth. The RBI also has powers tosupersede the BoM if the functioning of BoM is found unsatisfactory.
To report to BoDThe BoM will report to theBoard of Directors (BoD) andexercise oversight over thebankingrelated functions ofthe UCBs, assist the BoD on formulation of policies, and anyother related matter specifi��cally delegated to it by the BoD.
The functions of the BoM willinclude rendering expert advice on proposals being put upto the board or any committeeof the board for sanction of
loans; recommending actionfor recovery of bad loans, onetime settlement or compromise settlement and assisting theboard in monitoring the same;overseeing the management offunds and borrowings; and recommending proposals for investment of bank’s funds as perthe board approved policy.
The BoM will also have oversight on internal controls andsystems and risk management;exercise oversight on implementation of computerisation,technology adoption and otherincidental issues; oversee internal audit and inspectionfunctions, including compliance; exercise oversight oncomplaint redressal system;and assist the board in formulation of policies related tobanking.
All members of the BoM willconsist of persons having special knowledge or practical experience in respect of one ormore areas, such as accountancy, agriculture and rural economy, banking, cooperation,economics, fi��nance, law, smallscale industry, and IT.
RBI directs UCBs having deposits above ₹��100 cr to set up Board of Management
PRESS TRUST OF INDIA
New Delhi, December 31
Axis Bank, on Tuesday, appointed Amit Talgeri as theChief Risk Offi��cer (CRO) ofthe bank for three years fromJanuary 1. “Cyril Anand, theincumbent CRO, will retirefrom the services of thebank, with eff��ect from closeof business hours of December 31,” Axis Bank said in astatement.
Additionally, NaveenTahilyani has been appointed Group Executive, HeadBanking Operations andTransformation, with eff��ective from January 6. BothTahilyani and Talgeri will report to Managing Directorand CEO, Amitabh Chaudhry.
Axis Bank appointsChief Risk Officer
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/ைணX ெசயலாளT ெபG5பா�க5 இ.ராஜேசகT, AGவ�{T ேமR� மாவLட எ5.o.ஆT. மhறX ெசயலாளT AG3த# ேகா.அQ, ேசா|8கநVuT ேமR� ப�A ெசயலாளG5, சLடமhற $hனா� உS*WனGமான ேக.W. கNதh மRS5 ஏராளமான ேபT ஓ.பhtTெசVவ3ைத சNA3/ l3தா", வா>3/�கைள ெதQ)3/, வா>3/ ெபRறனT.
ெசhைன,ஜன.1– ெசhைன நகQh பல ப�Aக9V
A}ெரன மைழ ெபDத/. இதனாV சாைலக9V த"~T ேத8- =hற/.
சாNேதா5, பL%ண*பா�க5, ேகாட5பா�க5, -"%, ஆலN�T, ம%*பா�க5, ேமலவா�க5, ெபG8கள3�T, வ"டuT, /ைர*பா�க5, தா5பர5,ேகாட5பா�கக5 உ�9Lட பல ப�Aக9:5 ேலசான/ $தV eதமான மைழ ெபDத/.
தeழக3AV ெபDத மைழ அள& வGமாS:–
ச3Aயபாமா பVகைலகழக5 4
ெச.�., ெகாள*பா�க5, �h�T, ெசhைன )மான=ைலய5 3 ெச.�., ெச8கVபL,, ேசா98கT, தா5பர5, �NதமVj, �ெபG5l�T, ெசhைன, உ3Aரேம�T, மகாபjlர5 2 ெச.�.,ேகள5பா�க5, ேகா3த-Q, �Nதா WQL�, மHலா,/ைற, க ா L , மh ன ா T ே க ா ) V , காேவQ*பா�க5, காyOlர5, மர�காண5, �$�ண5 1ெச.�.,மைழ பAவா-_�ள/.
இ/�(3/ வாgைல ஆD& ைமய இய��னT பாலXசNAரh
iS5ேபா/, வ9ம"டல3Ah �> ப�AHV -ழ�� Aைச காRS5, ேமR� Aைச காRS5 சNA��5 ப�A தeழக ப�AHV =ல&-ற/. தeழக3AV ஓQG ப�Aக9V மைழ ெபD/�ள/. ெசhைன, lறநகT, காyOlர5, ேவuT மாவLட8க9V மைழ ெபD/�ள/. அ,3த 2 Aன8க\�� தeழக3AV ஓQG ப�Aக9V ேலசான/ $தV eதமான மைழ ெபDய வாD*l உ�ள/. ஜனவர 5 5 ேதA வைர மைழ ெதாடர வாD*l இG�-ற/ எhறாT.
ெச�ைனAB பரவலாக அ'FG ெகா/'ய மைழ
ஓ.ப45'ெச(வ$8,- அைம;ச'க� வா<$8
=�யாT�,ஜன.1–l3தா"%V உலக5 $zவ/5
3 லLச3/ 92 ஆHர3/ 78 �ழNைதக� WறNத =ைலHV அAV 17 சத?த �ழNைதக� இNAயா)V WறN/�ளன எhS _gெச* ெதQ)3/�ள/.
இNAயா $zவ/5 l3தா",� ெகா"டாLட8க� கைளகL% உ�ளன. 2020 ஜனவQ 1-5 ேதA, l3தா", Aன3தhS இNAயா)V 67 ஆHர3/ 385 �ழNைதக� WறN/�ளன எhS _gெச* ெதQ)3/�ள/. உலக5 $zவ/5 3 லLச3/ 92 ஆHர3/ 78 �ழNைதக� WறNத =ைலHV அAV 17 சத?த� �ழNைதக� இNAயா)V WறN/�ளன எhS _gெச* ெதQ)3/�ள/.
இAV பOW� ெபG8கடjV இG��5 Wo �)V 2020-5 ஆ"%h $தV �ழNைத WறN/�ள/. கைடOயாக அெமQ�கா)V �ழNைத WறN/�ள/. ஒL,ெமா3தமாக 3 லLச3/ 92 ஆHர5 �ழNைதக� 8 நா,க9V WறN/�ளன.
இAV இNAயா)V அAகபLசமாக 92 ஆHர3/ 78 �ழNைதக\5, அைத3 ெதாடTN/ �னா)V 46 ஆHர3/ 299 �ழNைதக\5, ைந�Qயா)V 26 ஆHர3/ 39 �ழNைதக\5, பா-[தாgV 16 ஆHர3/ 787 �ழNைதக\5, இNேதாேனOயா)V 13 ஆHர3/ 20 �ழNைதக\5 WறN/�ளன.
அெமQ�கா)V 10 ஆHர3/
452 �ழNைதக\5, கா8ேகா)V 10 ஆHர3/ 247 �ழNைதக\5, எ3Aேயா*Wயா)V 8 ஆHர3/ 493 �ழNைதக\5 WறN/�ளன.
உலக5 $zவ/5 l3தா", Aன3தhS WறNத �ழNைதக� �(3/ _gெச* அ(�ைக ெவ9HL,� ெகா"டா% வG-ற/. உலக* lக> ெபRற டா�டT ச3ேயNAர நா3 ேபா[, பாj&L நLச3Aர5 )3யா பாலh ஆ-ேயாG5 l3தா", Aன3தhS WறNதவTக�தாh.கடNத 2018-5 ஆ"%V 25 லLச5 �ழNைதக� WறNத ஒG மாத3/���ளாகேவ இறN/ )LடனT. ெபG5பாலான �ழNைதக� �ைற மாத3AV WறNததனா:5, O�கலான Wரசவ5, ெதாRS ேநாDக� ஆ-யைவ காரணமாக&5 உHQழN/�ளன. அேதசமய5, ஆ",ேதாS5 25 லLச3/��5 ேமலான �ழNைத க� இற�-hறன.கடNத Oல ஆ", களாக மG3/வ3AV ஏRபLட Oற*பான $hேனRற5 காரணமாக, WறN/ $தV மாத3AV இற��5 �ழNைதக9h எ"#�ைக 47 சத?த5 �ைறN/�ள/ �(*Wட3 த�க/.இ/�(3/ _gெசஃ* =Tவாக இய��நT ெஹhQLடா ேபாT iSைகHV, "l3தா",, lAய 10 ஆ",க9h ெதாட�க5. எATகால3ைத* பR(ய ந5$ைடய ந5W�ைகக�, அWலாைசக� மL,மVலா/, நம��*Wh வர�i%யவTகைள* பR(_5தாh" என3 ெதQ)3/�ளாT.
=.எ4.>.எஸ.#. -@A–-1 ேத'D E=Dக� ெவFGH
ெசhைன,ஜன.1–%எhWஎ[O ��*-1 ேதT&
$%&கைள தe>நா, அரm ப#யாளT ேதTவாைணய5 ெ வ 9 H L , � ள / . த e > ந ா , அரm* ப#யாளT ேதTவாைணய5 kல5 (%எhWஎ[O) ��* 1 ேதT&க� நட3த*பL, வG-hறன. அNத வைகHV 181 காj* ப#Hட8க\�� ��* 1 ேதT&க� நட3த*பLடன.
க ா j * ப # H ட 8 க \ � க ா ன எz3/ ேதT&க� $%Nத =ைலHV, ேநTகாணV நைடெபRற/. ேநTகாணV இhS (31.12.2019) $%வைடNத =ைலHV, இhேற $%&க� ெவ9Hட*பL,�ள/. ��*- 1 ேதT&�கான இSA $%&க� $தh$ைறயாக ஓரா"%R�� தe>நா, அரm* ப#யாளT ேதTவாைணய5 ெவ9HL,�ள/.
ஏJமைலயா4 தMசனO:ப,த'கQ,- இலவச ல"H
AG*பA,ஜன.1–AGமைல AG*பA ேதவ[தான5
20205 ஆ"%V ப�தTக\�� ஒG இgய அ()*ைப ெவ9HL,�ள/. AGமைல ஏzமைலயாைன தQO�க வG5 ஒFெவாG ப�தTக\��5 ஒG லL, Wரசாத5 இலவசமாக வழ8க ேதவ[தான5 $%& ெசD/�ள/. இNத lAய அ()*l ைவ�"ட ஏகாதO ஜனவQ 6 $தV அமV ப,3த*பட உ�ள/. தRேபா/ நைடபாைத வ|யாக வG5 ப�தTக\�� மL,ேம ேதவ[தான5 ஒG இலவச லLைட வழ8- வG-ற/.
அதhப% ஒGநாைள�� 20 ஆHர5 லL, Wரசாத5 இலவசமாக வழ8க*பL, வG-ற/. அைனவG��5 இலவச லL, எhற $ைறHV AனசQ 80 ஆHர5 லL, இலவசமாக வழ8க*பட உ�ள/. இதh kல5 ஒG மாத3AR� 24 லLச5 லL, Wரசாத5 வழ8க ேதவ[தான5 $%& ெசD/�ள/.
நா� ச�� !ர#கா% &'(�)நாைள�), &-ண/ப1
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)G/ ெபற நாைள��� (2–N ேதA) )"ண*W�க ேவ",5 எhS ெசhைன மாவLட கெல�டT �3தாலLme உ3தர)L,�ளாT.
ம3Aய அரசாV 2020–5 ஆ"%Rகான நாQ ச�A lர[காT )GAR�, ெப"க� $hேனRற3AR� OறNத சkக ேசைவ lQNத ெப"க�, ெசhைன மாவLட சkகநல அ:வலக5, மாவLட கெல�டT அ:வலக வளாக5, ெசhைன எhற $கவQHV அ4- உQய ப%வ5 ெபRS த�Nத ஆவண8க\டh i%ய கG3/G l3தக3/டh ப%வ5 �T3A ெசD/ 2–N ேதA மாைல��� மாவLட சkகநல அ:வலக3AV )"ண*ப5 ெசDA,மாS ேகL,� ெகா�ள*ப,-றாTக�. இ3தகவைல ெசhைன மாவLட கெல�டT �3தாலLme ெதQ)3/�ளாT.
2020 U$தா/H Vன$V( உலW( 3.92 ல"சO -ழ[ைதக� >ற[தன
l/ெடVj , ஜன.1–சாதாரண, எ�[Wர[, �9Tசாதன
ெரHVக\�கான பய#க� கLடண5 ஜனவQ 1–NேதA (இhS) $தV உயT3த*பLட/. lறநகT ெரHVக� மRS5 �சh %�ெகL கLடண5 உயT3த*பட)Vைல.
பய#க� ெரHV கLடண5 -.�.�� ஒG ைபசா $தV 4 ைபசா வைர உயT3த*பL,�ள/.
ெரHV கLடண5 கடNத Oல ஆ",களாக உயT3த*பட)Vைல. ெரHVேவHh 8 ேசைவகைள ஒh(ைண3/ இNAயh ெரHVேவ ேமலா"ைம =Sவன5 (ஐ.ஆT.எ5.எ[.) எhற ஒேர =Sவனமாக மாRற ம3Aய அைமXசரைவ அ"ைமHV ஒ*lதV அ93த/. இ/ேபால பVேவS O�கன நடவ%�ைககைள ெரHVேவ =Tவாக5 ேமRெகா", வG-ற/.
ெரHVேவ /ைறHV இNத =Aயா"%h 2-வ/ காலா"%V $Nைதய காலா"ைட)ட வGவாD �ைறN/�ள/. அதாவ/ பய#க� கLடண5 �.155 ேகா%_5 சர��
கLடண5 �.3,091 ேகா%_5 �ைறNAG�-ற/.
எனேவ வGவாைய உயT3த ெரHV கLடண5 $ைற*ப,3த*ப,5 எhS ெரHVேவ வாQய தைலவT ).ேக.யாதF Oல நாLக\�� $hl ெதQ)3தாT. இைத3 ெதாடTN/ பய#க� ெரHV கLடண5 O(தள& உயT3த*பL,�ள/.
இ/ெதாடTபாக ரHVேவ =Tவாக5 ேநRS ெவ9HLட அ()*WV i(HG*பதாவ/:–
lறநகT ெரHVக9V கLடண5 உயT3த*பட)Vைல. அேதேபால ெரHV %�ெகL $hபA& கLடண5, �*பT பா[L ெரHVக\�கான i,தV கLடண5, �சh %�ெகL ஆ-யவR(V எNத மாSத:5 இVைல. இAV ஏRகனேவ உ�ள கLடண8கேள ெதாடG5.
�9Tசாதன வசA (ஏ.O.) இVலாத சாதாரண ெரHVக9V இர"டா5 வ�*l, �8�5 வசA, $தV வ�*l ஆ-ய கLடண8க� ஒG -ேலா �LடG�� ஒG ைபசா உயT3த*ப,-ற/.
ெர]( க"டண உய'D:இ4` Eத( அமa,- வ[த8
� � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �� � � � � � � � � ! " # � � � � � � � � $ % & � � � � � � ' � � � � ( � � � $ % & � � ) � * � � � % � ) � � � � � � � � � � � + � � , � � � � + - � �# � � � . / � � ) � � $ � / 0 & � � " � � $ � � 1 � � � � � ) � � � � � �2 3 � � � ! � � � � � � � � 4 � � 2 3 � � 5 � & � � � � � � $ � 2 3 � 6 � � % � � �
ெமHV மRS5 எ�[Wர[ ெரHVக9V ஏ.O. வசA இVலாத இர"டா5 வ�*l, �8�5 வசA, $தV வ�*l ஆ-ய கLடண8க� ஒG -ேலா �LடG�� 2 ைபசா உயT3த*ப,-ற/.
அேதேபால ெமHV மRS5 எ�[Wர[ ெரHVக9V ஏO ேசTகாT, $தV வ�*l 3 டயT/3இ, ஏO 2 டயT, ஏO $தV வ�*l/இO/இஏ உ�9Lட அைன3/ ஏ.O. வசA கLடண8க� ஒG -ேலா �LடG�� 4 ைபசா உயT3த*ப,-ற/. இNத கLடண உயT& ேநRS ந�9ர& 12 ம# $தV (ஜனவQ 1 $தV) அம:�� வNத/. ஏRகனேவ பைழய கLடண3AV %�ெகL வா8-யவTக� 1-N ேதAேயா அVல/ அதR� Whனேரா உயT3த*பLட i,தV கLடண3ைத ெச:3த ேவ"%யAVைல. அேதசமய5 1-N ேதA அVல/ அதR� WhனT ெரHV =ைலய8க9ேலா, ெரHjV %�ெகL பQேசாதகTக9டேமா %�ெகL வா8�பவTக� உயT3த*பLட கLடண3ைத ெச:3த ேவ",5.
ரா�தாg, சதா*A, /ரNேதா, வNேத பார3, ேதஜ[, ஹ5சபT, மஹாமனா, ைகமாh, அNேயாதயா, கQ*ர3, ஜh சதா*A, ரா�ய ரா#, _வ எ�[Wர[, m)தா மRS5 Oற*l கLடண ெரHVக\��5 இNத கLடண உயT& ெபாGN/5.
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உதாரண3/�� ெசhைனHV இGN/ ெநVைல ெசVவதR� (650 -ேலா �LடT) சாதாரண ெரHVக9V கLடண5 mமாT �.6.50-5, ெமHV, எ�[Wர[ ெரHVக9V ஏ.O.வசA இVலாத கLடண5 �.13-5, ஏ.O. வசA�� கLடண5 �.26-5 i,தலாக ெச:3த ேவ",5.
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